An Analytical Approach to the Protein Designability Problem

We present an analytical method for determining the designability of protein structures. We apply our method to the case of two-dimensional lattice structures, and give a systematic solution for the spectrum of any structure. Using this spectrum, the designability of a structure can be estimated. We outline a heirarchy of structures, from most to least designable, and show that this heirarchy depends on the potential that is used.Comment: 16 pages 4 figure

Fast and accurate prediction of protein side-chain conformations

Summary: We developed a fast and accurate side-chain modeling program [Optimized Side Chain Atomic eneRgy (OSCAR)-star] based on orientation-dependent energy functions and a rigid rotamer model. The average computing time was 18 s per protein for 218 test proteins with higher prediction accuracy (1.1% increase for χ1 and 0.8% increase for χ1+2) than the best performing program developed by other groups. We show that the energy functions, which were calibrated to tolerate the discrete errors of rigid rotamers, are appropriate for protein loop selection, especially for decoys without extensive structural refinement

Learning probabilistic models of hydrogen bond stability from molecular dynamics simulation trajectories

Hydrogen bonds (H-bonds) play a key role in both the formation and stabilization of protein structures. H-bonds involving atoms from residues that are close to each other in the main-chain sequence stabilize secondary structure elements. H-bonds between atoms from distant residues stabilize a protein’s tertiary structure. However, H-bonds greatly vary in stability. They form and break while a protein deforms. For instance, the transition of a protein from a nonfunctional to a functional state may require some H-bonds to break and others to form. The intrinsic strength of an individual H-bond has been studied from an energetic viewpoint, but energy alone may not be a very good predictor. Other local interactions may reinforce (or weaken) an H-bond. This paper describes inductive learning methods to train a protein-independent probabilistic model of H-bond stability from molecular dynamics (MD) simulation trajectories. The training data describes H-bond occurrences at successive times along these trajectories by the values of attributes called predictors. A trained model is constructed in the form of a regression tree in which each non-leaf node is a Boolean test (split) on a predictor. Each occurrence of an H-bond maps to a path in this tree from the root to a leaf node. Its predicted stability is associated with the leaf node. Experimental results demonstrate that such models can predict H-bond stability quite well. In particular, their performance is roughly 20 % better than that of models based on H-bond energy alone. In addition, they can accurately identify a large fraction of the least stable H-bonds in a give

PVS: a web server for protein sequence variability analysis tuned to facilitate conserved epitope discovery

We have developed PVS (Protein Variability Server), a web-based tool that uses several variability metrics to compute the absolute site variability in multiple protein-sequence alignments (MSAs). The variability is then assigned to a user-selected reference sequence consisting of either the first sequence in the alignment or a consensus sequence. Subsequently, PVS performs tasks that are relevant for structure-function studies, such as plotting and visualizing the variability in a relevant 3D-structure. Neatly, PVS also implements some other tasks that are thought to facilitate the design of epitope discovery-driven vaccines against pathogens where sequence variability largely contributes to immune evasion. Thus, PVS can return the conserved fragments in the MSA—as defined by a user-provided variability threshold—and locate them in a relevant 3D-structure. Furthermore, PVS can return a variability-masked sequence, which can be directly submitted to the RANKPEP server for the prediction of conserved T-cell epitopes. PVS is freely available at: http://imed.med.ucm.es/PVS/

PROTDES: CHARMM toolbox for computational protein design

We present an open-source software able to automatically mutate any residue positions and find the best aminoacids in an arbitrary protein structure without requiring pairwise approximations. Our software, PROTDES, is based on CHARMM and it searches automatically for mutations optimizing a protein folding free energy. PROTDES allows the integration of molecular dynamics within the protein design. We have implemented an heuristic optimization algorithm that iteratively searches the best aminoacids and their conformations for an arbitrary set of positions within a structure. Our software allows CHARMM users to perform protein design calculations and to create their own procedures for protein design using their own energy functions. We show this by implementing three different energy functions based on different solvent treatments: surface area accessibility, generalized Born using molecular volume and an effective energy function. PROTDES, a tutorial, parameter sets, configuration tools and examples are freely available at http://soft.synth-bio.org/protdes.html

Development and validation of a tool to improve community pharmacists' surveillance role in the safe dispensing of herbal supplements

Background: There has been an appreciable increase in the use of herbal supplements, including immune boosters, during the current COVID-19 pandemic. However, there are concerns with falsified herbal supplements Objectives Developed a new questionnaire that can potentially help community pharmacists with identifying the extent of falsified herbal supplements. Methods: A cross sectional study conducted over nine months among 500 community pharmacies in the UAE. Face-to-face interviews were undertaken using a structured questionnaire, which was subjected to face and content validity, with the content validity index (CVI) computed. Construct validity was tested using an exploratory factor analysis (EFA) via principally component analysis (PCA). The model was then confirmed through Partial confirmatory factor analysis (PCFA). Reliability was assessed via test-retest reliability, internal consistency, item internal consistency (IIC), and intraclass correlation coefficients (ICCs). Results: An instrument compromised of five domains with a 24-item scale was developed with CVI of 0.843. The KMO measure of sampling adequacy was 0.891, and Bartlett’s test of sphericity indicated significance (p-value < 0.001). Confirmation of the subsequent 5-domains was achieved through PCFA using MLA with oblimin rotation. The PCFA obtained values of 0.962 for NFI, 0.977 for CFI, and 0.987 for TLI; all values were greater than 0.95, and the RMSEA value was 0.03 (i.e., less than 0.06). Consequently, the model had a good fit. All domains demonstrated Cronbach’s alpha coefficients above 0.70, with 0.940 for the full instrument. Meanwhile, all items met the IIC correlation standard of ≥ 0.40. The instrument presented good ICC statistics of 0.940 (0.928 – 0.950) as well as statistical significance (p < 0.001). Those participants who had more than 10 experience years more likely to identify falsified herbal supplements compared to those who have 1 to 10 experience years (p < 0.001). Conclusions: This study developed and validated a new instrument to identify safe herbal supplements products, which will help enhance the role of the community pharmacists in safe and effective treatment of suitable patients with herbal supplements

Knowledge-based energy functions for computational studies of proteins

This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design. We discuss in some details about the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and non-linear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed.Comment: 57 pages, 6 figures. To be published in a book by Springe

Stabilisation of the Fc Fragment of Human IgG1 by Engineered Intradomain Disulfide Bonds

We report the stabilization of the human IgG1 Fc fragment by engineered intradomain disulfide bonds. One of these bonds, which connects the N-terminus of the CH3 domain with the F-strand, led to an increase of the melting temperature of this domain by 10°C as compared to the CH3 domain in the context of the wild-type Fc region. Another engineered disulfide bond, which connects the BC loop of the CH3 domain with the D-strand, resulted in an increase of Tm of 5°C. Combined in one molecule, both intradomain disulfide bonds led to an increase of the Tm of about 15°C. All of these mutations had no impact on the thermal stability of the CH2 domain. Importantly, the binding of neonatal Fc receptor was also not influenced by the mutations. Overall, the stabilized CH3 domains described in this report provide an excellent basic scaffold for the engineering of Fc fragments for antigen-binding or other desired additional or improved properties. Additionally, we have introduced the intradomain disulfide bonds into an IgG Fc fragment engineered in C-terminal loops of the CH3 domain for binding to Her2/neu, and observed an increase of the Tm of the CH3 domain for 7.5°C for CysP4, 15.5°C for CysP2 and 19°C for the CysP2 and CysP4 disulfide bonds combined in one molecule

Computational Design of a PDZ Domain Peptide Inhibitor that Rescues CFTR Activity

The cystic fibrosis transmembrane conductance regulator (CFTR) is an epithelial chloride channel mutated in patients with cystic fibrosis (CF). The most prevalent CFTR mutation, ΔF508, blocks folding in the endoplasmic reticulum. Recent work has shown that some ΔF508-CFTR channel activity can be recovered by pharmaceutical modulators (“potentiators” and “correctors”), but ΔF508-CFTR can still be rapidly degraded via a lysosomal pathway involving the CFTR-associated ligand (CAL), which binds CFTR via a PDZ interaction domain. We present a study that goes from theory, to new structure-based computational design algorithms, to computational predictions, to biochemical testing and ultimately to epithelial-cell validation of novel, effective CAL PDZ inhibitors (called “stabilizers”) that rescue ΔF508-CFTR activity. To design the “stabilizers”, we extended our structural ensemble-based computational protein redesign algorithm to encompass protein-protein and protein-peptide interactions. The computational predictions achieved high accuracy: all of the top-predicted peptide inhibitors bound well to CAL. Furthermore, when compared to state-of-the-art CAL inhibitors, our design methodology achieved higher affinity and increased binding efficiency. The designed inhibitor with the highest affinity for CAL (kCAL01) binds six-fold more tightly than the previous best hexamer (iCAL35), and 170-fold more tightly than the CFTR C-terminus. We show that kCAL01 has physiological activity and can rescue chloride efflux in CF patient-derived airway epithelial cells. Since stabilizers address a different cellular CF defect from potentiators and correctors, our inhibitors provide an additional therapeutic pathway that can be used in conjunction with current methods