SCWRL and MolIDE: computer programs for side-chain conformation prediction and homology modeling

Characterization of hyperactive mutations in the renal potassium channel ROMK uncovers unique effects on channel biogenesis and ion conductance

Hypertension affects one billion people worldwide and is the most common risk factor for cardiovascular disease, yet a comprehensive picture of its underlying genetic factors is incomplete. Amongst regulators of blood pressure is the renal outer medullary potassium (ROMK) channel. While select ROMK mutants are prone to premature degradation and lead to disease, heterozygous carriers of some of these same alleles are protected from hypertension. Therefore, we hypothesized that gain-of-function (GoF) ROMK variants which increase potassium flux may predispose people to hypertension. To begin to test this hypothesis, we employed genetic screens and a candidate-based approach to identify six GoF variants in yeast. Subsequent functional assays in higher cells revealed two variant classes. The first group exhibited greater stability in the endoplasmic reticulum, enhanced channel assembly, and/or increased protein at the cell surface. The second group of variants resided in the PIP2-binding pocket, and computational modeling coupled with patch-clamp studies demonstrated lower free energy for channel opening and slowed current rundown, consistent with an acquired PIP2-activated state. Together, these findings advance our understanding of ROMK structure-function, suggest the existence of hyperactive ROMK alleles in humans, and establish a system to facilitate the development of ROMK-targeted antihypertensives.

Clinical and genetic spectrum of Ataxia Telangiectasia Tunisian patients: Bioinformatic analysis unveil mechanisms of ATM variants pathogenicity

Ataxia Telangiectasia (AT) is a rare multisystemic neurodegenerative disease caused by biallelic mutations in the ATM gene. Few clinical studies on AT disease have been conducted in Tunisia, however, the mutational landscape is still undefined. Our aim is to determine the clinical and genetic spectrum of AT Tunisian patients and to explore the potential underlying mechanism of variant pathogenicity. Sanger sequencing was performed for nine AT patients. A comprehensive computational analysis was conducted to evaluate the possible pathogenic effect of ATM identified variants. Genetic screening of ATM gene has identified nine different variants from which six have not been previously reported. In silico analysis have predicted a pathogenic effect of identified mutations. This was corroborated by a structural bioinformatics study based on molecular modeling and docking for novel missense mutations. Our findings suggest a profound impact of identified mutations not only on the ATM protein stability, but also on the ATM-ligand interactions. Our study characterizes the mutational landscape of AT Tunisian patients which will allow to set up genetic counseling and prenatal diagnosis for families at risk and expand the spectrum of ATM variants worldwide. Furthermore, understanding the mechanism that underpin variant pathogenicity could provide further insights into disease pathogenesis.

SLiMAn 2.0: meaningful navigation through peptide-protein interaction networks

Among the myriad of protein-protein interactions occurring in living organisms, a substantial amount involves small linear motifs (SLiMs) recognized by structured domains. However, predictions of SLiM-based networks are tedious, due to the abundance of such motifs and a high portion of false positive hits. For this reason, a webserver SLiMAn (Short Linear Motif Analysis) was developed to focus the search on the most relevant SLiMs. Using SLiMAn, one can navigate into a given (meta-)interactome and tune a variety of parameters associated to each type of SLiMs in attempt to identify functional ELM motifs and their recognition domains. The IntAct and BioGRID databases bring experimental information, while IUPred and AlphaFold provide boundaries of folded and disordered regions. Post-translational modifications listed in PhosphoSite+ are highlighted. Links to PubMed accelerate scrutiny into the literature, to support (or not) putative pairings. Dedicated visualization features are also incorporated, such as Cytoscape for macromolecular networks and BINANA for intermolecular contacts within structural models generated by SCWRL 3.0. The use of SLiMAn 2.0 is illustrated on a simple example. It is freely available at https://sliman2.cbs.cnrs.fr.

Deep learning for the PSIPRED Protein Analysis Workbench

The PSIRED Workbench is a long established and popular bioinformatics web service offering a wide range of machine learning based analyses for characterizing protein structure and function. In this paper we provide an update of the recent additions and developments to the webserver, with a focus on new Deep Learning based methods. We briefly discuss some trends in server usage since the publication of AlphaFold2 and we give an overview of some upcoming developments for the service. The PSIPRED Workbench is available at http://bioinf.cs.ucl.ac.uk/psipred.

Three-Dimensional Interaction Homology: Deconstructing Residue-Residue and Residue-Lipid Interactions in Membrane Proteins

A method is described to deconstruct the network of hydropathic interactions within and between a protein's sidechain and its environment into residue-based three-dimensional maps. These maps encode favorable and unfavorable hydrophobic and polar interactions, in terms of spatial positions for optimal interactions, relative interaction strength, as well as character. In addition, these maps are backbone angle-dependent. After map calculation and clustering, a finite number of unique residue sidechain interaction maps exist for each backbone conformation, with the number related to the residue's size and interaction complexity. Structures for soluble proteins (~749,000 residues) and membrane proteins (~387,000 residues) were analyzed, with the latter group being subdivided into three subsets related to the residue's position in the membrane protein: soluble domain, core-facing transmembrane domain, and lipid-facing transmembrane domain. This work suggests that maps representing residue types and their backbone conformation can be reassembled to optimize the medium-to-high resolution details of a protein structure. In particular, the information encoded in maps constructed from the lipid-facing transmembrane residues appears to paint a clear picture of the protein-lipid interactions that are difficult to obtain experimentally.

Technological advancements in viral vector designing and optimization for therapeutic applications

Viral vector engineering is critical to the advancement of several sectors of biotechnology, gene therapy, and vaccine development. These vectors were produced from viruses, were employed to deliver therapeutic genes or to alter biological processes. The potential for viral vectors to improve the precision, safety, and efficiency of therapeutic interventions has boosted their demand. The dynamic interplay between technological advancements and computational tools in establishing the landscape of viral vector engineering and vector optimization for therapeutic reasons is discussed in this chapter. It also emphasizes the importance of in silico techniques in maximizing vector potential for therapeutics and many phases of viral vector engineering, from genomic analysis to computer modelling and advancements to improve precise gene delivery. High-throughput screening propels the expedited process of vector selection, and computational techniques to analyze complex omics data to further enhance vector capabilities have been discussed. As in silico models reveal insights into off-target effects and integration sites, vector safety (biodistribution and toxicity) remains a crucial part and bridges the gap between preclinical and clinical investigations. Despite the limitations, this chapter depicts a future in which technology and computing merge to catapult viral vector therapy into an era of boundless possibilities.

Assessment of Two Restraint Potentials for Coarse-Grained Chemical-Cross-Link-Assisted Modeling of Protein Structures

The influence of distance restraints from chemical cross-link mass spectroscopy (XL-MS) on the quality of protein structures modeled with the coarse-grained UNRES force field was assessed by using a protocol based on multiplexed replica exchange molecular dynamics, in which both simulated and experimental cross-link restraints were employed, for 23 small proteins. Six cross-links with upper distance boundaries from 4 Å to 12 Å (azido benzoic acid succinimide (ABAS), triazidotriazine (TATA), succinimidyldiazirine (SDA), disuccinimidyl adipate (DSA), disuccinimidyl glutarate (DSG), and disuccinimidyl suberate (BS3)) and two types of restraining potentials ((i) simple flat-bottom Lorentz-like potentials dependent on side chain distance (all cross-links) and (ii) distance- and orientation-dependent potentials determined based on molecular dynamics simulations of model systems (DSA, DSG, BS3, and SDA)) were considered. The Lorentz-like potentials with properly set parameters were found to produce a greater number of higher-quality models compared to unrestrained simulations than the MD-based potentials, because the latter can force too long distances between side chains. Therefore, the flat-bottom Lorentz-like potentials are recommended to represent cross-link restraints. It was also found that significant improvement of model quality upon the introduction of cross-link restraints is obtained when the sum of differences of indices of cross-linked residues exceeds 150.

Exploring the inhibitory potential of in silico-designed small peptides on Helicobacter pylori Hp0231 (DsbK), a periplasmic oxidoreductase involved in disulfide bond formation

Introduction: Helicobacter pylori is a bacterium that colonizes the gastric epithelium, which affects millions of people worldwide. H. pylori infection can lead to various gastrointestinal diseases, including gastric adenocarcinoma and mucosa-associated lymphoid tissue lymphoma. Conventional antibiotic therapies face challenges due to increasing antibiotic resistance and patient non-compliance, necessitating the exploration of alternative treatment approaches. In this study, we focused on Hp0231 (DsbK), an essential component of the H. pylori Dsb (disulfide bond) oxidative pathway, and investigated peptide-based inhibition as a potential therapeutic strategy. Methods: Three inhibitory peptides designed by computational modeling were evaluated for their effectiveness using a time-resolved fluorescence assay. We also examined the binding affinity between Hp0231 and the peptides using microscale thermophoresis. Results and discussion: Our findings demonstrate that in silico-designed synthetic peptides can effectively inhibit Hp0231-mediated peptide oxidation. Targeting Hp0231 oxidase activity could attenuate H. pylori virulence without compromising bacterial viability. Therefore, peptide-based inhibitors of Hp0231 could be candidates for the development of new targeted strategy, which does not influence the composition of the natural human microbiome, but deprive the bacterium of its pathogenic properties.

Detection and Analysis of Short Linear Motif-Based Protein-Protein Interactions with SLiMAn2 Web Server

Interactomics is bringing a deluge of data regarding protein-protein interactions (PPIs) which are involved in various molecular processes in all types of cells. However, this information does not easily translate into direct and precise molecular interfaces. This limits our understanding of each interaction network and prevents their efficient modulation. A lot of the detected interactions involve recognition of short linear motifs (SLiMs) by a folded domain while others rely on domain-domain interactions. Functional SLiMs hide among a lot of spurious ones, making deeper analysis of interactomes tedious. Hence, actual contacts and direct interactions are difficult to identify.Consequently, there is a need for user-friendly bioinformatic tools, enabling rapid molecular and structural analysis of SLiM-based PPIs in a protein network. In this chapter, we describe the use of the new webserver SLiMAn to help digging into SLiM-based PPIs in an interactive fashion.

Long-Time Dynamics of Selected Molecular-Motor Components Using a Physics-Based Coarse-Grained Approach

Molecular motors are essential for the movement and transportation of macromolecules in living organisms. Among them, rotatory motors are particularly efficient. In this study, we investigated the long-term dynamics of the designed left-handed alpha/alpha toroid (PDB: 4YY2), the RBM2 flagellum protein ring from Salmonella (PDB: 6SD5), and the V-type Na+-ATPase rotor in Enterococcus hirae (PDB: 2BL2) using microcanonical and canonical molecular dynamics simulations with the coarse-grained UNRES force field, including a lipid-membrane model, on a millisecond laboratory time scale. Our results demonstrate that rotational motion can occur with zero total angular momentum in the microcanonical regime and that thermal motions can be converted into net rotation in the canonical regime, as previously observed in simulations of smaller cyclic molecules. For 6SD5 and 2BL2, net rotation (with a ratcheting pattern) occurring only about the pivot of the respective system was observed in canonical simulations. The extent and direction of the rotation depended on the initial conditions. This result suggests that rotatory molecular motors can convert thermal oscillations into net rotational motion. The energy from ATP hydrolysis is required probably to set the direction and extent of rotation. Our findings highlight the importance of molecular-motor structures in facilitating movement and transportation within living organisms.

In silico insights into procathepsin S maturation mediated by glycosaminoglycans

Procathepsins, inactive precursors of cathepsins are present in the extracellular matrix (ECM) and in lysosomes. Their active forms are involved in a number of biologically relevant processes, including bone resorption, intracellular proteolysis and regulation of programmed cell death. These processes might be mediated by glycosaminoglycans (GAGs), long unbranched periodic negatively charged polysaccharides. GAGs are also present in ECM and play important role in anticoagulation, angiogenesis and tissue regeneration. GAGs not only mediate the enzymatic activity of cathepsins but can also regulate the process of procathepsin maturation, as it was shown for procathepsin B and S. In this study, we propose the molecular mechanism underlying the biological role of GAGs in procathepsin S maturation and compare our findings with computational data obtained for procathepsin B. We rigorously analyse procathepsin S-GAG complexes in terms of their dynamics, free energy and potential allosteric regulation. We conclude that the GAG binding region might have an effect on the dynamics of procathepsin S structure and so affect its maturation by two different mechanisms.

3D interaction homology: The hydrophobic residues alanine, isoleucine, leucine, proline and valine play different structural roles in soluble and membrane proteins

The aliphatic hydrophobic amino acid residues—alanine, isoleucine, leucine, proline and valine—are among the most common found in proteins. Their structural role in proteins is seemingly obvious: engage in hydrophobic interactions to stabilize secondary, and to a lesser extent, tertiary and quaternary structure. However, favorable hydrophobic interactions involving the sidechains of these residue types are generally less significant than the unfavorable set arising from interactions with polar atoms. Importantly, the constellation of interactions between residue sidechains and their environments can be recorded as three-dimensional maps that, in turn, can be clustered. The clustered average map sets compose a library of interaction profiles encoding interaction strengths, interaction types and the optimal 3D position for the interacting partners. This library is backbone angle-dependent and suggests solvent and lipid accessibility for each unique interaction profile. In this work, in addition to analysis of soluble proteins, a large set of membrane proteins that contained optimized artificial lipids were evaluated by parsing the structures into three distinct components: soluble extramembrane domain, lipid facing transmembrane domain, core transmembrane domain. The aliphatic residues were extracted from each of these sets and passed through our calculation protocol. Notable observations include: the roles of aliphatic residues in soluble proteins and in the membrane protein’s soluble domains are nearly identical, although the latter are slightly more solvent accessible; by comparing maps calculated with sidechain-lipid interactions to maps ignoring those interactions, the potential extent of residue-lipid and residue-interactions can be assessed and likely exploited in structure prediction and modeling; amongst these residue types, the levels of lipid engagement show isoleucine as the most engaged, while the other residues are largely interacting with neighboring helical residues.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Improvements and new functionalities of UNRES server for coarse-grained modeling of protein structure, dynamics, and interactions

In this paper we report the improvements and extensions of the UNRES server (https://unres-server.chem.ug.edu.pl) for physics-based simulations with the coarse-grained UNRES model of polypeptide chains. The improvements include the replacement of the old code with the recently optimized one and adding the recent scale-consistent variant of the UNRES force field, which performs better in the modeling of proteins with the β and the α+β structures. The scope of applications of the package was extended to data-assisted simulations with restraints from nuclear magnetic resonance (NMR) and chemical crosslink mass-spectroscopy (XL-MS) measurements. NMR restraints can be input in the NMR Exchange Format (NEF), which has become a standard. Ambiguous NMR restraints are handled without expert intervention owing to a specially designed penalty function. The server can be used to run smaller jobs directly or to prepare input data to run larger production jobs by using standalone installations of UNRES.

Origin of Correlations between Local Conformational States of Consecutive Amino Acid Residues and Their Role in Shaping Protein Structures and in Allostery

By analyzing the Kubo-cluster-cumulant expansion of the potential of mean force of polypeptide chains corresponding to backbone-local interactions averaged over the rotation of the peptide groups about the C^α···C^α virtual bonds, we identified two important kinds of "along-chain" correlations that pertain to extended chain segments bordered by turns (usually the β-strands) and to the folded spring-like segments (usually α-helices), respectively, and are expressed as multitorsional potentials. These terms affect the positioning of structural elements with respect to each other and, consequently, contribute to determining their packing. Additionally, for extended chain segments, the correlation terms contribute to propagating the conformational change at one end to the other end, which is characteristic of allosteric interactions. We confirmed both findings by statistical analysis of the virtual-bond geometry of 77 950 proteins. Augmenting coarse-grained and, possibly, all-atom force fields with these correlation terms could improve their capacity to model protein structure and dynamics.

Ribonucleic Acid Folding Prediction Based on Iterative Multiscale Simulation

RNA folding prediction is a challenge. Currently, many RNA folding models are coarse-grained (CG) with the potential derived from the known RNA structures. However, this potential is not suitable for modified and entirely new RNA. It is also not suitable for the folding simulation of RNA in the real cellular environment, including many kinds of molecular interactions. In contrast, our proposed model has the potential to address these issues, which is a multiscale simulation scheme based on all-atom (AA) force fields. We fit the CG force field using the trajectories generated by the AA force field and then iteratively perform molecular dynamics (MD) simulations of the two scales. The all-atom molecular dynamics (AAMD) simulation is mainly responsible for the correction of RNA structure, and the CGMD simulation is mainly responsible for efficient conformational sampling. On the basis of this scheme, we can successfully fold three RNAs belonging to a hairpin, a pseudoknot, and a four-way junction.

Exploiting Sequence-Dependent Rotamer Information in Global Optimization of Proteins

Rotamers, namely amino acid side chain conformations common to many different peptides, can be compiled into libraries. These rotamer libraries are used in protein modeling, where the limited conformational space occupied by amino acid side chains is exploited. Here, we construct a sequence-dependent rotamer library from simulations of all possible tripeptides, which provides rotameric states dependent on adjacent amino acids. We observe significant sensitivity of rotamer populations to sequence and find that the library is successful in locating side chain conformations present in crystal structures. The library is designed for applications with basin-hopping global optimization, where we use it to propose moves in conformational space. The addition of rotamer moves significantly increases the efficiency of protein structure prediction within this framework, and we determine parameters to optimize efficiency.

Characterization of Synovial Fluid Components: Albumin-Chondroitin Sulfate Interactions Seen through Molecular Dynamics

The friction coefficient of articular cartilage (AC) is very low. A method of producing tailor-made materials with even similar lubrication properties is still a challenge. The physicochemical reasons for such excellent lubrication properties of AC are still not fully explained; however, a crucial factor seems to be synergy between synovial fluid (SF) components. As a stepping stone to being able to produce innovative materials characterized by a very low friction coefficient, we studied the interactions between two important components of SF: human serum albumin (HSA) and chondroitin sulfate (CS). The molecular dynamics method, preceded by docking, is used in the study. Interactions of HSA with two types of CS (IV and VI), with the addition of three types of ions often found in physiological solutions: Ca2+, Na+, and Mg2+, are compared. It was found that there were differences in the energy of binding values and interaction maps between CS-4 and CS-6 complexes. HSA:CS-4 complexes were bound stronger than in the case of HSA:CS-6 because more interactions were formed across all types of interactions except one-the only difference was for ionic bridges, which were more often found in HSA:CS-6 complexes. RMSD and RMSF indicated that complexes HSA:CS-4 behave much more stably than HSA:CS-6. The type of ions added to the solution was also very important and changed the interaction map. However, the biggest difference was caused by the addition of Ca2+ ions which were prone to form ionic bridges.

A coarse-grained approach to NMR-data-assisted modeling of protein structures

The ESCASA algorithm for analytical estimation of proton positions from coarse-grained geometry developed in our recent work has been implemented in modeling protein structures with the highly coarse-grained UNRES model of polypeptide chains (two sites per residue) and nuclear magnetic resonance (NMR) data. A penalty function with the shape of intersecting gorges was applied to treat ambiguous distance restraints, which automatically selects consistent restraints. Hamiltonian replica exchange molecular dynamics was used to carry out the conformational search. The method was tested with both unambiguous and ambiguous restraints producing good-quality models with GDT_TS from 7.4 units higher to 14.4 units lower than those obtained with the CYANA or MELD software for protein-structure determination from NMR data at the all-atom resolution. The method can thus be applied in modeling the structures of flexible proteins, for which extensive conformational search enabled by coarse-graining is more important than high modeling accuracy.

Prediction of Aggregation of Biologically-Active Peptides with the UNRES Coarse-Grained Model

The UNited RESidue (UNRES) model of polypeptide chains was applied to study the association of 20 peptides with sizes ranging from 6 to 32 amino-acid residues. Twelve of those were potentially aggregating hexa- or heptapeptides excised from larger proteins, while the remaining eight contained potentially aggregating sequences, functionalized by attaching larger ends rich in charged residues. For 13 peptides, the experimental data of aggregation were used. The remaining seven were synthesized, and their properties were measured in this work. Multiplexed replica-exchange simulations of eight-chain systems were conducted at 12 temperatures from 260 to 370 K at concentrations from 0.421 to 5.78 mM, corresponding to the experimental conditions. The temperature profiles of the fractions of monomers and octamers showed a clear transition corresponding to aggregate dissociation. Low simulated transition temperatures were obtained for the peptides, which did not precipitate after incubation, as well as for the H-GNNQQNY-NH2 prion–protein fragment, which forms small fibrils. A substantial amount of inter-strand β-sheets was found in most of the systems. The results suggest that UNRES simulations can be used to assess peptide aggregation except for glutamine- and asparagine-rich peptides, for which a revision of the UNRES sidechain–sidechain interaction potentials appears necessary.

Mechanical Stability of Ribonuclease A Heavily Depends on the Redox Environment

Disulfide bonds are covalent bonds that connect nonlocal fragments of proteins, and they are unique post-translational modifications of proteins. They require the oxidizing environment to be stable, which occurs for example during oxidative stress; however, in a cell the reductive environment is maintained, lowering their stability. Despite many years of research on disulfide bonds, their role in the protein life cycle is not fully understood and seems to strictly depend on a system or process in which they are involved. In this article, coarse-grained UNited RESidue (UNRES), and all-atom Assisted Model Building with Energy Refinement (AMBER) force fields were applied to run a series of steered molecular dynamics (SMD) simulations of one of the most studied, but still not fully understood, proteins─ribonuclease A (RNase A). SMD simulations were performed to study the mechanical stability of RNase A in different oxidative-reductive environments. As disulfide bonds (and any other covalent bonds) cannot break/form in any classical all-atom force field, we applied additional restraints between sulfur atoms of reduced cysteines which were able to mimic the breaking of the disulfide bonds. On the other hand, the coarse-grained UNRES force field enables us to study the breaking/formation of the disulfide bonds and control the reducing/oxidizing environment owing to the presence of the designed distance/orientation-dependent potential. This study reveals that disulfide bonds have a strong influence on the mechanical stability of RNase A only in a highly oxidative environment. However, the local stability of the secondary structure seems to play a major factor in the overall stability of the protein. Both our thermal unfolding and mechanical stretching studies show that the most stable disulfide bond is Cys65-Cys72. The breaking of disulfide bonds Cys26-Cys84 and Cys58-Cys110 is associated with large force peaks. They are structural bridges, which are mostly responsible for stabilizing the RNase A conformation, while the presence of the remaining two bonds (Cys65-Cys72 and Cys40-Cys95) is most likely connected with the enzymatic activity rather than the structural stability of RNase A in the cytoplasm. Our results prove that disulfide bonds are indeed stabilizing fragments of the proteins, but their role is strongly redox environment-dependent.

SLiMAn: An Integrative Web Server for Exploring Short Linear Motif-Mediated Interactions in Interactomes

Cells express thousands of macromolecules, and their functioning relies on multiple networks of intermolecular interactions. These interactions can be experimentally determined at different spatial and temporal resolutions. But, physical interfaces are not often delineated directly, especially in high-throughput experiments. A large fraction of protein-protein interactions involves domain and so-called SLiMs (for Short Linear Motifs). Often, SLiMs lie in disordered regions or loops. Their small size, limited sequence conservation, and loosely folded nature prevent straightforward detection. SLiMAn (Short Linear Motif Analysis), a new web server, is provided to help thorough analysis of interactomics data. From a list of putative interactants (e.g., output from an interactomics study), SLiMs (from ELM) and SLiM-recognition domains (from Pfam) are extracted, and putative pairings are displayed. Predicted results can be filtered using motif E-values, IUPred2 scores, or BioGRID interaction matches. When structural templates are available, a given SLiM and its recognition domain can be modeled using SCWRL. We illustrate here the use of SLiMAn on distinct examples, including one real-case study. We oversee wide-range applications for SLiMAn in the context of the massive analysis of protein-protein interactions. This new web server is made freely available at https://sliman.cbs.cnrs.fr.

FOXM1-mediated activation of phospholipase D1 promotes lipid droplet accumulation and reduces ROS to support paclitaxel resistance in metastatic cancer cells

Chemoresistance is a major challenge for the treatment of cancer with metastasis. We investigated the mechanisms of lipid metabolites involved in drug resistance. Here, metastatic cancer cells isolated from mouse models were resistant to paclitaxel treatment in vitro and in vivo when compared with parental cancer cells. FOXM1, an oncogenic transcriptional factor, was highly expressed in metastatic cancer cells, and overexpression of FOXM1 conferred parental cancer cells resistance to paclitaxel. Lipidomic analysis showed that FOXM1 increased unsaturated triglyceride (TG) and phosphatidylcholine (PC) abundance, which are the main components of lipid droplet (LD). Inhibition of LD formation sensitized cells to paclitaxel. Mechanistically, the enzyme phospholipase D1 (PLD1) was identified as a potential effector target of FOXM1. PLD1 promoted LD accumulation, which reduced the level of reactive oxygen species (ROS) and maintained endoplasmic reticulum (ER) homeostasis in resistant cells with the treatment of paclitaxel. Moreover, inhibition of PLD1 reversed FOXM1-conferred paclitaxel resistance in vitro and in vivo. This study, for the first time, reveals the role of FOXM1-mediated PLD1 in LD accumulation and paclitaxel resistance. Targeting PLD1 or LD formation may help reverse chemoresistance in metastatic cancer cells. Generally, our results identified FOXM1 as a driver of paclitaxel resistance via activation of PLD1 to promote of LD accumulation, which contributes to the maintenace of ER homeostasis when metastatic cancer cells are confronted with ROS induced by paclitaxel.

Modeling the Structure, Dynamics, and Transformations of Proteins with the UNRES Force Field

The physics-based united-residue (UNRES) model of proteins ( www.unres.pl ) has been designed to carry out large-scale simulations of protein folding. The force field has been derived and parameterized based on the principles of statistical-mechanics, which makes it independent of structural databases and applicable to treat nonstandard situations such as, proteins that contain D-amino-acid residues. Powered by Langevin dynamics and its replica-exchange extensions, UNRES has found a variety of applications, including ab initio and database-assisted protein-structure prediction, simulating protein-folding pathways, exploring protein free-energy landscapes, and solving biological problems. This chapter provides a summary of UNRES and a guide for potential users regarding the application of the UNRES package in a variety of research tasks.

NOG-Derived Peptides Can Restore Neuritogenesis on a CRASH Syndrome Cell Model

Homo- and heterophilic binding mediated by the immunoglobulin (Ig)-like repeats of cell adhesion molecules play a pivotal role in cell-cell and cell-extracellular matrix interactions. L1CAM is crucial to neuronal differentiation, in both mature and developing nervous systems, and several studies suggest that its functional interactions are mainly mediated by Ig2–Ig2 binding. X-linked mutations in the human L1CAM gene are summarized as L1 diseases, including the most diagnosed CRASH neurodevelopmental syndrome. In silico simulations provided a molecular rationale for CRASH phenotypes resulting from mutations I179S and R184Q in the homophilic binding region of Ig2. A synthetic peptide reproducing such region could both mimic the neuritogenic capacity of L1CAM and rescue neuritogenesis in a cellular model of the CRASH syndrome, where the full L1CAM ectodomain proved ineffective. Presented functional evidence opens the route to the use of L1CAM-derived peptides as biotechnological and therapeutic tools.

CRB1-Related Retinal Dystrophies in a Cohort of 50 Patients: A Reappraisal in the Light of Specific Müller Cell and Photoreceptor CRB1 Isoforms

Pathogenic variants in CRB1 lead to diverse recessive retinal disorders from severe Leber congenital amaurosis to isolated macular dystrophy. Until recently, no clear phenotype-genotype correlation and no appropriate mouse models existed. Herein, we reappraise the phenotype-genotype correlation of 50 patients with regards to the recently identified CRB1 isoforms: a canonical long isoform A localized in Müller cells (12 exons) and a short isoform B predominant in photoreceptors (7 exons). Twenty-eight patients with early onset retinal dystrophy (EORD) consistently had a severe Müller impairment, with variable impact on the photoreceptors, regardless of isoform B expression. Among them, two patients expressing wild type isoform B carried one variant in exon 12, which specifically damaged intracellular protein interactions in Müller cells. Thirteen retinitis pigmentosa patients had mainly missense variants in laminin G-like domains and expressed at least 50% of isoform A. Eight patients with the c.498_506del variant had macular dystrophy. In one family homozygous for the c.1562C>T variant, the brother had EORD and the sister macular dystrophy. In contrast with the mouse model, these data highlight the key role of Müller cells in the severity of CRB1-related dystrophies in humans, which should be taken into consideration for future clinical trials.

Molecular Docking Simulation Studies Identifies Potential Natural Product Derived-Antiwolbachial Compounds as Filaricides against Onchocerciasis

Onchocerciasis is the leading cause of blindness and severe skin lesions which remain a major public health problem, especially in tropical areas. The widespread use of antibiotics and the long duration required for effective treatment continues to add to the increasing global menace of multi-resistant pathogens. Onchocerca volvulus harbors the endosymbiont bacteria Wolbachia, essential for the normal development of embryos, larvae and long-term survival of the adult worm, O. volvulus. We report here results of using structure-based drug design (SBDD) approach aimed at identifying potential novel Wolbachia inhibitors from natural products against the Wolbachia surface protein (WSP). The protein sequence of the WSP with UniProtKB identifier Q0RAI4 was used to model the three-dimensional (3D) structure via homology modelling techniques using three different structure-building algorithms implemented in Modeller, I-TASSER and Robetta. Out of the 15 generated models of WSP, one was selected as the most reasonable quality model which had 82, 15.5, 1.9 and 0.5% of the amino acid residues in the most favored regions, additionally allowed regions, generously allowed regions and disallowed regions, respectively, based on the Ramachandran plot. High throughput virtual screening was performed via Autodock Vina with a library comprising 42,883 natural products from African and Chinese databases, including 23 identified anti-Onchocerca inhibitors. The top six compounds comprising ZINC000095913861, ZINC000095486235, ZINC000035941652, NANPDB4566, acetylaleuritolic acid and rhemannic acid had binding energies of −12.7, −11.1, −11.0, −11, −10.3 and −9.5 kcal/mol, respectively. Molecular dynamics simulations including molecular mechanics Poisson-Boltzmann (MMPBSA) calculations reinforced the stability of the ligand-WSP complexes and plausible binding mechanisms. The residues Arg45, Tyr135, Tyr148 and Phe195 were predicted as potential novel critical residues required for ligand binding in pocket 1. Acetylaleuritolic acid and rhemannic acid (lantedene A) have previously been shown to possess anti-onchocercal activity. This warrants the need to evaluate the anti-WSP activity of the identified molecules. The study suggests the exploitation of compounds which target both pockets 1 and 2, by investigating their potential for effective depletion of Wolbachia. These compounds were predicted to possess reasonably good pharmacological profiles with insignificant toxicity and as drug-like. The compounds were computed to possess biological activity including antibacterial, antiparasitic, anthelmintic and anti-rickettsials. The six natural products are potential novel antiwolbachial agents with insignificant toxicities which can be explored further as filaricides for onchocerciasis.

Current Approaches in Supersecondary Structures Investigation

Proteins expressed during the cell cycle determine cell function, topology, and responses to environmental influences. The development and improvement of experimental methods in the field of structural biology provide valuable information about the structure and functions of individual proteins. This work is devoted to the study of supersecondary structures of proteins and determination of their structural motifs, description of experimental methods for their detection, databases, and repositories for storage, as well as methods of molecular dynamics research. The interest in the study of supersecondary structures in proteins is due to their autonomous stability outside the protein globule, which makes it possible to study folding processes, conformational changes in protein isoforms, and aberrant proteins with high productivity.

A conserved Neurite Outgrowth and Guidance motif with biomimetic potential in neuronal Cell Adhesion Molecules

The discovery of conserved protein motifs can, in turn, unveil important regulatory signals, and when properly designed, synthetic peptides derived from such motifs can be used as biomimetics for biotechnological and therapeutic purposes. We report here that specific Ig-like repeats from the extracellular domains of neuronal Cell Adhesion Molecules share a highly conserved Neurite Outgrowth and Guidance (NOG) motif, which mediates homo- and heterophilic interactions crucial in neural development and repair. Synthetic peptides derived from the NOG motif of such proteins can boost neuritogenesis, and this potential is also retained by peptides with recombinant sequences, when fitting the NOG sequence pattern. The NOG motif discovery not only provides one more tile to the complex puzzle of neuritogenesis, but also opens the route to new neural regeneration strategies via a tunable biomimetic toolbox.

Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors

Olfactory receptors (ORs) constitute the largest superfamily of G protein-coupled receptors (GPCRs). ORs are involved in sensing odorants as well as in other ectopic roles in non-nasal tissues. Matching of an enormous number of the olfactory stimulation repertoire to its counterpart OR through machine learning (ML) will enable understanding of olfactory system, receptor characterization, and exploitation of their therapeutic potential. In the current study, we have selected two broadly tuned ectopic human OR proteins, OR1A1 and OR2W1, for expanding their known chemical space by using molecular descriptors. We present a scheme for selecting the optimal features required to train an ML-based model, based on which we selected the random forest (RF) as the best performer. High activity agonist prediction involved screening five databases comprising ~23 M compounds, using the trained RF classifier. To evaluate the effectiveness of the machine learning based virtual screening and check receptor binding site compatibility, we used docking of the top target ligands to carefully develop receptor model structures. Finally, experimental validation of selected compounds with significant docking scores through in vitro assays revealed two high activity novel agonists for OR1A1 and one for OR2W1.

Modularity-based parallel protein design algorithm with an implementation using shared memory programming

Given a target protein structure, the prime objective of protein design is to find amino acid sequences that will fold/acquire to the given three-dimensional structure. The protein design problem belongs to the non-deterministic polynomial-time-hard class as sequence search space increases exponentially with protein length. To ensure better search space exploration and faster convergence, we propose a protein modularity-based parallel protein design algorithm. The modular architecture of the protein structure is exploited by considering an intermediate structural organization between secondary structure and domain defined as protein unit (PU). Here, we have incorporated a divide-and-conquer approach where a protein is split into PUs and each PU region is explored in a parallel fashion. It has been further analyzed that our shared memory implementation of modularity-based parallel sequence search leads to better search space exploration compared to the case of traditional full protein design. Sequence-based analysis on design sequences depicts an average of 39.7% sequence similarity on the benchmark data set. Structure-based comparison of the modeled structures of the design protein with the target structure exhibited an average root-mean-square deviation of 1.17 Å and an average template modeling score of 0.89. The selected modeled structures of the design protein sequences are validated using 100 ns molecular dynamics simulations where 80% of the proteins have shown better or similar stability to the respective target proteins. Our study informs that our modularity-based protein design algorithm can be extended to protein interaction design as well.

Modeling protein structures with the coarse-grained UNRES force field in the CASP14 experiment

The UNited RESidue (UNRES) force field was tested in the 14th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP14), in which larger oligomeric and multimeric targets were present compared to previous editions. Three prediction modes were tested (i) ab initio (the UNRES group), (ii) contact-assisted (the UNRES-contact group), and (iii) template-assisted (the UNRES-template group). For most of the targets, the contact restraints were derived from the server models top-ranked by the DeepQA method, while the DNCON2 method was used for 11 targets. Our consensus-fragment procedure was used to run template-assisted predictions. Each group also processed the Nuclear Magnetic Resonance (NMR)- and Small Angle X-Ray Scattering (SAXS)-data assisted targets. The average Global Distance Test Total Score (GDT_TS) of the 'Model 1' predictions were 29.17, 39.32, and 56.37 for the UNRES, UNRES-contact, and UNRES-template predictions, respectively, increasing by 0.53, 2.24, and 3.76, respectively, compared to CASP13. It was also found that the GDT_TS of the UNRES models obtained in ab initio mode and in the contact-assisted mode decreases with the square root of chain length, while the exponent in this relationship is 0.20 for the UNRES-template group models and 0.11 for the best performing AlphaFold2 models, which suggests that incorporation of database information, which stems from protein evolution, brings in long-range correlations, thus enabling the correction of force-field inaccuracies.

Pseudopotentials for coarse-grained cross-link-assisted modeling of protein structures

Pseudopotentials for the chemical cross-links comprising the glutamic- and aspartic-acid side chains bridged with adipic- (ADH) or pimelic-acid hydrazide (PDH), and the lysine side chains bridged with glutaric (BS² G) or suberic acid (BS³ ) for coarse-grained cross-link-assisted simulations were determined by canonical molecular dynamics with the Amber14sb force field. The potentials depend on the distance between side-chain ends and on side-chain orientation, this preventing from making cross-link contacts across the globule in simulations. The potentials were implemented in the UNRES coarse-grained force field and their effect on the quality of models was assessed with 11 monomeric and 1 dimeric proteins, using synthetic or experimental cross-link data. Simulations with the new potentials resulted in improvement of the generated models compared to unrestrained simulations in more instances compared to those with the statistical potentials.

Discovery of Arylsulfonamides as Dual Orexin Receptor Agonists

Loss of orexin-producing neurons results in narcolepsy with cataplexy, and orexin agonists have been shown to increase wakefulness and alleviate narcolepsy symptoms in animal models. Several OX2R agonists have been reported but with little or no activity at OX1R. We conducted structure-activity relationship studies on the OX2R agonist YNT-185 (2) and discovered dual agonists such as RTOXA-43 (40) with EC₅₀'s of 24 nM at both OX2R and OX1R. Computational modeling studies based on the agonist-bound OX2R cryogenic electron microscopy structures showed that 40 bound in the same binding pocket and interactions of the pyridylmethyl group of 40 with OX1R may have contributed to its high OX1R potency. Intraperitoneal injection of 40 increased time awake, decreased time asleep, and increased sleep/wake consolidation in 12-month old mice. This work provides a promising dual small molecule agonist and supports development of orexin agonists as potential treatments for orexin-deficient disorders such as narcolepsy.

Crystal structure of dopamine D1 receptor in complex with G protein and a non-catechol agonist

Dopamine D1 receptor (D1R) is an important drug target implicated in many psychiatric and neurological disorders. Selective agonism of D1R are sought to be the therapeutic strategy for these disorders. Most selective D1R agonists share a dopamine-like catechol moiety in their molecular structure, and their therapeutic potential is therefore limited by poor pharmacological properties in vivo. Recently, a class of non-catechol D1R selective agonists with a distinct scaffold and pharmacological properties were reported. Here, we report the crystal structure of D1R in complex with stimulatory G protein (Gs) and a non-catechol agonist Compound 1 at 3.8 Å resolution. The structure reveals the ligand bound to D1R in an extended conformation, spanning from the orthosteric site to extracellular loop 2 (ECL2). Structural analysis reveals that the unique features of D1R ligand binding pocket explains the remarkable selectivity of this scaffold for D1R over other aminergic receptors, and sheds light on the mechanism for D1R activation by the non-catechol agonist.

Protein Structure Prediction: Conventional and Deep Learning Perspectives

Protein structure prediction is a way to bridge the sequence-structure gap, one of the main challenges in computational biology and chemistry. Predicting any protein's accurate structure is of paramount importance for the scientific community, as these structures govern their function. Moreover, this is one of the complicated optimization problems that computational biologists have ever faced. Experimental protein structure determination methods include X-ray crystallography, Nuclear Magnetic Resonance Spectroscopy and Electron Microscopy. All of these are tedious and time-consuming procedures that require expertise. To make the process less cumbersome, scientists use predictive tools as part of computational methods, using data consolidated in the protein repositories. In recent years, machine learning approaches have raised the interest of the structure prediction community. Most of the machine learning approaches for protein structure prediction are centred on co-evolution based methods. The accuracy of these approaches depends on the number of homologous protein sequences available in the databases. The prediction problem becomes challenging for many proteins, especially those without enough sequence homologs. Deep learning methods allow for the extraction of intricate features from protein sequence data without making any intuitions. Accurately predicted protein structures are employed for drug discovery, antibody designs, understanding protein-protein interactions, and interactions with other molecules. This article provides a review of conventional and deep learning approaches in protein structure prediction. We conclude this review by outlining a few publicly available datasets and deep learning architectures currently employed for protein structure prediction tasks.

Rational design of cannabinoid type-1 receptor allosteric modulators: Org27569 and PSNCBAM-1 hybrids

Allosteric modulation offers an alternate approach to target the cannabinoid type-1 receptor (CB₁) for therapeutic benefits. Examination of the two widely studied prototypic CB₁ negative allosteric modulators (NAMs) Org27569 and PSNCBAM-1 revealed structural resemblance and similar structure-activity relationships (SARs). In silico docking and dynamics simulation studies using the crystal structure of CB₁ co-bound with CP55,940 and Org27569 suggested that Org27569 and PSNCBAM-1 occupied the same binding pocket and several common interactions were present in both series with the CB₁ receptor. A new scaffold was therefore designed by merging the key structural features from the two series and the hybrids retained these binding features in the in silico docking studies. In addition, one such hybrid displayed similar functions to Org27569 in dynamic simulations by preserving a key R214^3.50-D338^6.30 salt bridge and maintaining an antagonist-like Helix3-Helix6 interhelical distance. Based on these results, a series of hybrids were synthesized and assessed in calcium mobilization, [³⁵S]GTPγS binding and cAMP assays. Several compounds displayed comparable potencies to Org27569 and PSNCBAM-1 in these assays. This work offers new insight of the SAR requirement at the allosteric site of the CB₁ receptor and provides a new scaffold that can be optimized for the development of future CB₁ allosteric modulators.

Adaptively Iterative Multiscale Switching Simulation Strategy and Applications to Protein Folding and Structure Prediction

Structure prediction is an important means to quickly understand new protein functions. However, the prediction of effects of proteins that have no detectable templates is still to be improved. Molecular dynamics simulation is supposed to be the primary research tool for structure predictions, but it still has limitations of huge computational cost in all-atom (AA) models and rough accuracy in coarse-grained (CG) models. We propose a universal multiscale simulation strategy named AIMS in which simulations can iteratively switch among multiple resolutions in order to adaptively trade off AA accuracy and CG high-efficiency. AIMS follows the idea of CG-guided enhanced sampling so that final results always keep AA accuracy. We successfully achieve four ab initio and four data-assisted protein structure predictions using AIMS. The prediction result is an ensemble rather than a structure and provides special insights on folding metastable states. AIMS is estimated to achieve a computational speed about 40 times faster than that of conventional AA simulations.

An Evolutionary Profile Guided Greedy Parallel Replica-Exchange Monte Carlo Search Algorithm for Rapid Convergence in Protein Design

Protein design, also known as the inverse protein folding problem, is the identification of a protein sequence that folds into a target protein structure. Protein design is proved as an NP-hard problem. While researchers are working on designing heuristics with an emphasis on new scoring functions, we propose a replica-exchange Monte Carlo (REMC) search algorithm that ensures faster convergence using a greedy strategy. Using biological insights, we construct an evolutionary profile to encode the amino acid variability in different positions of the target protein from its structural homologs. The evolutionary profile guides the REMC search, and the greedy approach confirms appreciable exploration and exploitation of the sequence-structure fitness surface. We allow termination of a simulation trajectory once stagnant situation is detected. A series of sequence and structure level validations establish the goodness of our design. On a benchmark dataset, our algorithm reports an average root-mean-square deviation of 1.21Å between the target and the design proteins when modeled with an existing protein folding software. Besides, our algorithm assures 6.16 times overall speedup. In Molecular Dynamics simulations, we observe that four out of selected five design proteins report better to comparable stability to the corresponding target proteins.

Predicting new protein conformations from molecular dynamics simulation conformational landscapes and machine learning

Molecular dynamics (MD) simulations are a popular method of studying protein structure and function, but are unable to reliably sample all relevant conformational space in reasonable computational timescales. A range of enhanced sampling methods are available that can improve conformational sampling, but these do not offer a complete solution. We present here a proof-of-principle method of combining MD simulation with machine learning to explore protein conformational space. An autoencoder is used to map snapshots from MD simulations onto a user-defined conformational landscape defined by principal components analysis or specific structural features, and we show that we can predict, with useful accuracy, conformations that are not present in the training data. This method offers a new approach to the prediction of new low energy/physically realistic structures of conformationally dynamic proteins and allows an alternative approach to enhanced sampling of MD simulations.

ModFlex: Towards Function Focused Protein Modeling

There is a wide, and continuously widening, gap between the number of proteins known only by their amino acid sequence versus those structurally characterized by direct experiment. To close this gap, we mostly rely on homology-based inference and modeling to reason about the structures of the uncharacterized proteins by using structures of homologous proteins as templates. With the rapidly growing size of the Protein Data Bank, there are often multiple choices of templates, including multiple sets of coordinates from the same protein. The substantial conformational differences observed between different experimental structures of the same protein often reflect function related structural flexibility. Thus, depending on the questions being asked, using distant homologs, or coordinate sets with lower resolution but solved in the appropriate functional form, as templates may be more informative. The ModFlex server (https://modflex.org/) addresses this seldom mentioned gap in the standard homology modeling approach by providing the user with an interface with multiple options and tools to select the most relevant template and explore the range of structural diversity in the available templates. ModFlex is closely integrated with a range of other programs and servers developed in our group for the analysis and visualization of protein structural flexibility and divergence.

Molecular Cloning, Structure and Phylogenetic Analysis of a Hemocyanin Subunit from the Black Sea Crustacean Eriphia verrucosa (Crustacea, Malacostraca)

Hemocyanins are copper-binding proteins that play a crucial role in the physiological processes in crustaceans. In this study, the cDNA encoding hemocyanin subunit 5 from the Black sea crab Eriphia verrucosa (EvHc5) was cloned using EST analysis, RT-PCR and rapid amplification of the cDNA ends (RACE) approach. The full-length cDNA of EvHc5 was 2254 bp, consisting of a 5′ and 3′ untranslated regions and an open reading frame of 2022 bp, encoding a protein consisting of 674 amino acid residues. The protein has an N-terminal signal peptide of 14 amino acids as is expected for proteins synthesized in hepatopancreas tubule cells and secreted into the hemolymph. The 3D model showed the presence of three functional domains and six conserved histidine residues that participate in the formation of the copper active site in Domain 2. The EvHc5 is O-glycosylated and the glycan is exposed on the surface of the subunit similar to Panulirus interruptus. The phylogenetic analysis has shown its close grouping with γ-type of hemocyanins of other crustacean species belonging to order Decapoda, infraorder Brachyura.

Comparative Analysis of Bacteriophytochrome Agp2 and Its Engineered Photoactivatable NIR Fluorescent Proteins PAiRFP1 and PAiRFP2

Two photoactivatable near infrared fluorescent proteins (NIR FPs) named “PAiRFP1” and “PAiRFP2” are formed by directed molecular evolution from Agp2, a bathy bacteriophytochrome of Agrobacterium tumefaciens C58. There are 15 and 24 amino acid substitutions in the structure of PAiRFP1 and PAiRFP2, respectively. A comprehensive molecular exploration of these bacteriophytochrome photoreceptors (BphPs) are required to understand the structure dynamics. In this study, the NIR fluorescence emission spectra for PAiRFP1 were recorded upon repeated excitation and the fluorescence intensity of PAiRFP1 tends to increase as the irradiation time was prolonged. We also predicted that mutations Q168L, V244F, and A480V in Agp2 will enhance the molecular stability and flexibility. During molecular dynamics (MD) simulations, the average root mean square deviations of Agp2, PAiRFP1, and PAiRFP2 were found to be 0.40, 0.49, and 0.48 nm, respectively. The structure of PAiRFP1 and PAiRFP2 were more deviated than Agp2 from its native conformation and the hydrophobic regions that were buried in PAiRFP1 and PAiRFP2 core exposed to solvent molecules. The eigenvalues and the trace of covariance matrix were found to be high for PAiRFP1 (597.90 nm²) and PAiRFP2 (726.74 nm²) when compared with Agp2 (535.79 nm²). It was also found that PAiRFP1 has more sharp Gibbs free energy global minima than Agp2 and PAiRFP2. This comparative analysis will help to gain deeper understanding on the structural changes during the evolution of photoactivatable NIR FPs. Further work can be carried out by combining PCR-based directed mutagenesis and spectroscopic methods to provide strategies for the rational designing of these PAiRFPs.

Artificial protein sequences enable recognition of vicinal and distant protein functional relationships

High divergence in protein sequences makes the detection of distant protein relationships through homology-based approaches challenging. Grouping protein sequences into families, through similarities in either sequence or 3-D structure, facilitates in the improved recognition of protein relationships. In addition, strategically designed protein-like sequences have been shown to bridge distant structural domain families by serving as artificial linkers. In this study, we have augmented a search database of known protein domain families with such designed sequences, with the intention of providing functional clues to domain families of unknown structure. When assessed using representative query sequences from each family, we obtain a success rate of 94% in protein domain families of known structure. Further, we demonstrate that the augmented search space enabled fold recognition for 582 families with no structural information available a priori. Additionally, we were able to provide reliable functional relationships for 610 orphan families. We discuss the application of our method in predicting functional roles through select examples for DUF4922, DUF5131, and DUF5085. Our approach also detects new associations between families that were previously not known to be related, as demonstrated through new sub-groups of the RNA polymerase domain among three distinct RNA viruses. Taken together, designed sequences-augmented search databases direct the detection of meaningful relationships between distant protein families. In turn, they enable fold recognition and offer reliable pointers to potential functional sites that may be probed further through direct mutagenesis studies.

Immunoinformatics and Structural Analysis for Identification of Immunodominant Epitopes in SARS-CoV-2 as Potential Vaccine Targets

A new coronavirus infection, COVID-19, has recently emerged, and has caused a global pandemic along with an international public health emergency. Currently, no licensed vaccines are available for COVID-19. The identification of immunodominant epitopes for both B- and T-cells that induce protective responses in the host is crucial for effective vaccine design. Computational prediction of potential epitopes might significantly reduce the time required to screen peptide libraries as part of emergent vaccine design. In our present study, we used an extensive immunoinformatics-based approach to predict conserved immunodominant epitopes from the proteome of SARS-CoV-2. Regions from SARS-CoV-2 protein sequences were defined as immunodominant, based on the following three criteria regarding B- and T-cell epitopes: (i) they were both mapped, (ii) they predicted protective antigens, and (iii) they were completely identical to experimentally validated epitopes of SARS-CoV. Further, structural and molecular docking analyses were performed in order to understand the binding interactions of the identified immunodominant epitopes with human major histocompatibility complexes (MHC). Our study provides a set of potential immunodominant epitopes that could enable the generation of both antibody- and cell-mediated immunity. This could contribute to developing peptide vaccine-based adaptive immunotherapy against SARS-CoV-2 infections and prevent future pandemic outbreaks.

Engineered P450 BM3 and cpADH5 coupled cascade reaction for β-oxo fatty acid methyl ester production in whole cells

Hydroxy- or ketone- functionalized fatty acid methyl esters (FAMEs) are important compounds for production of pharmaceuticals, vitamins, cosmetics or dietary supplements. Biocatalysis through enzymatic cascades has drawn attention to the efficient, sustainable, and greener synthetic processes. Furthermore, whole cell catalysts offer important advantages such as cofactor regeneration by cell metabolism, omission of protein purification steps and increased enzyme stability. Here, we report the first whole cell catalysis employing an engineered P450 BM3 variant and cpADH5 coupled cascade reaction for the biosynthesis of hydroxy- and keto-FAMEs. Firstly, P450 BM3 was engineered through the KnowVolution approach yielding P450 BM3 variant YE_M1_2, (R47S/Y51W/T235S/N239R/I401 M) which exhibited boosted performance toward methyl hexanoate. The initial oxidation rate of YE_M1_2 toward methyl hexanoate was determined to be 23-fold higher than the wild type enzyme and a 1.5-fold increase in methyl 3-hydroxyhexanoate production was obtained (YE_M1_2; 2.75 mM and WT; 1.8 mM). Subsequently, the whole cell catalyst for the synthesis of methyl 3-hydroxyhexanoate and methyl 3-oxohexanoate was constructed by combining the engineered P450 BM3 and cpADH5 variants in an artificial operon. A 2.06 mM total product formation was achieved by the whole cell catalyst including co-expressed channel protein, FhuA and co-solvent addition. Moreover, the generated whole cell biocatalyst also accepted methyl valerate, methyl heptanoate as well as methyl octanoate as substrates and yielded ω-1 ketones as the main product.

Ten quick tips for homology modeling of high-resolution protein 3D structures

The purpose of this quick guide is to help new modelers who have little or no background in comparative modeling yet are keen to produce high-resolution protein 3D structures for their study by following systematic good modeling practices, using affordable personal computers or online computational resources. Through the available experimental 3D-structure repositories, the modeler should be able to access and use the atomic coordinates for building homology models. We also aim to provide the modeler with a rationale behind making a simple list of atomic coordinates suitable for computational analysis abiding to principles of physics (e.g., molecular mechanics). Keeping that objective in mind, these quick tips cover the process of homology modeling and some postmodeling computations such as molecular docking and molecular dynamics (MD). A brief section was left for modeling nonprotein molecules, and a short case study of homology modeling is discussed.

Role of Glycosaminoglycans in Procathepsin B Maturation: Molecular Mechanism Elucidated by a Computational Study

Procathepsins are an inactive, immature form of cathepsins, predominantly cysteine proteases present in the extracellular matrix (ECM) and in lysosomes that play a key role in various biological processes such as bone resorption or intracellular proteolysis. The enzymatic activity of cathepsins can be mediated by glycosaminoglycans (GAGs), long unbranched periodic negatively charged polysaccharides found in ECM that take part in many biological processes such as anticoagulation, angiogenesis, and tissue regeneration. In addition to the known effects on mature cathepsins, GAGs can mediate the maturation process of procathepsins, in particular, procathepsin B. However, the detailed mechanism of this mediation at the molecular level is still unknown. In this study, for the first time, we aimed to unravel the role of GAGs in this process using computational approaches. We rigorously analyzed procathepsin B–GAG complexes in terms of their dynamics, energetics, and potential allosteric regulation. We revealed that GAGs can stabilize the conformation of the procathepsin B structure with the active site accessible for the substrate and concluded that GAGs most probably bind to procathepsin B once the zymogen adopts the enzymatically active conformation. Our data provided a novel mechanistic view of the maturation process of procathepsin B, while the approaches elaborated here might be useful to study other procathepsins. Furthermore, our data can serve as a rational guide for experimental work on procathepsin–GAG systems that are not characterized in vivo and in vitro yet.

Effects of reverse genetic mutations on the spectral and photochemical behavior of a photoactivatable fluorescent protein PAiRFP1

Bacteriophytochrome photoreceptors (BphPs) containing biliverdin (BV) have great potential for the development of genetically engineered near-infrared fluorescent proteins (NIR FPs). We investigated a photoactivatable fluorescent protein PAiRFP1, was engineered through directed molecular evolution. The coexistence of both red light absorbing (Pr) and far-red light absorbing (Pfr) states in dark is essential for the photoactivation of PAiRFP1. The PCR based site-directed reverse mutagenesis, spectroscopic measurements and molecular dynamics (MD) simulations were performed on three targeted sites V386A, V480A and Y498H in PHY domain to explore their potential effects during molecular evolution of PAiRFP1. We found that these substitutions did not affect the coexistence of Pr and Pfr states but led to slight changes in the photophysical parameters. The covalent docking of biliverdin (cis and trans form) with PAiRFP1 was followed by several 100 ns MD simulations to provide some theoretical explanations for the coexistence of Pr and pfr states. The results suggested that experimentally observed coexistence of Pr and Pfr states in both PAiRFP1 and mutants were resulted from the improved stability of Pr state. The use of experimental and computational work provided useful understanding of Pr and Pfr states and the effects of these mutations on the photophysical properties of PAiRFP1.

Improved Consensus-Fragment Selection in Template-Assisted Prediction of Protein Structures with the UNRES Force Field in CASP13

The method for protein-structure prediction, which combines the physics-based coarse-grained UNRES force field with knowledge-based modeling, has been developed further and tested in the 13th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP13). The method implements restraints from the consensus fragments common to server models. In this work, the server models to derive fragments have been chosen on the basis of quality assessment; a fully automatic fragment-selection procedure has been introduced, and Dynamic Fragment Assembly pseudopotentials have been fully implemented. The Global Distance Test Score (GDT_TS), averaged over our “Model 1” predictions, increased by over 10 units with respect to CASP12 for the free-modeling category to reach 40.82. Our “Model 1” predictions ranked 20 and 14 for all and free-modeling targets, respectively (upper 20.2% and 14.3% of all models submitted to CASP13 in these categories, respectively), compared to 27 (upper 21.1%) and 24 (upper 18.9%) in CASP12, respectively. For oligomeric targets, the Interface Patch Similarity (IPS) and Interface Contact Similarity (ICS) averaged over our best oligomer models increased from 0.28 to 0.36 and from 12.4 to 17.8, respectively, from CASP12 to CASP13, and top-ranking models of 2 targets (H0968 and T0997o) were obtained (none in CASP12). The improvement of our method in CASP13 over CASP12 was ascribed to the combined effect of the overall enhancement of server-model quality, our success in selecting server models and fragments to derive restraints, and improvements of the restraint and potential-energy functions.