Tunable Pentapeptide Self-Assembled β-Sheet Hydrogels.

Oligopeptide-based supramolecular hydrogels hold promise in a range of applications. The gelation of these systems is hard to control, with minor alterations in the peptide sequence significantly influencing the self-assembly process. We explored three pentapeptide sequences with different charge distributions and discovered that they formed robust, pH-responsive hydrogels. By altering the concentration and charge distribution of the peptide sequence, the stiffness of the hydrogels could be tuned across two orders of magnitude (2-200 kPa). Also, through reassembly of the β-sheet interactions the hydrogels could self-heal and they demonstrated shear-thin behavior. Using spectroscopic and cryo-imaging techniques, we investigated the relationship between peptide sequence and molecular structure, and how these influence the mechanical properties of the hydrogel. These pentapeptide hydrogels with tunable morphology and mechanical properties have promise in tissue engineering, injectable delivery vectors, and 3D printing applications

An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research

Advances in Protein Design: Conformational Switch, Multimeric, and Protein-DNA Design

The aim of protein design is to produce sequences that fold into a desired structure with improved or novel properties. Since the problem exhibits degeneracy, where many sequences can fold into the same structure, it is important to have design tools that can explore a large number of sequences. This thesis presents a series of computational protein design tools that expand the capabilities of quadratic assignment-like protein design methods to the design of conformational switches, multimeric systems, and protein-DNA binding. For the conformational switch design problem, an optimization model is introduced to design for sequences that change folds with a minimum number of mutations. Designed sequences are then computationally validated by a transition specificity metric that uses a detailed electrostatic energy function. This method is validated by an experimental test set and experimental results presented. Further, the detailed electrostatic energy function is shown to improve the accuracy of other validation metrics. For multimeric protein design, a molecular dynamics (MD) based procedure is presented for producing flexible templates for multimeric systems. These templates can be used in designing multimeric systems. The resulting sequences can be validated computationally using a multimeric fold specificity method and an MD-based approximate association affinity metric. This method was applied to the design of ultrasmall self-associating peptides, self-associating FG-repeat peptides, and CXCR4/CCR5 dual inhibitors. Experimental validation of the self-associating peptides and dual inhibitors are presented. For the protein-DNA design, a novel protein-DNA optimization model is introduced which accounts for both protein-protein and protein-DNA interactions. Resulting designed sequences are validated by fold specificity and a protein-DNA interaction energy metric. This method was applied to the design of a prototype foamy virus integrase for binding specificity and computational results presented. The integration of these methods into the automated Protein WISDOM framework is important to the wider academic community. The webtool is presented along with strategies for integrating the developed methods into the framework. An application of the protein design method to the design of methyltransferase inhibitors is presented. The methods introduced represent the expansion of the quadratic assignment-like protein design methods to a wider range of biologically relevant problems

Protein folding and de novo protein design for biotechnological applications

In the postgenomic era, the medical/biological fields are advancing faster than ever. However, before the power of full-genome sequencing can be fully realized, the connection between amino acid sequence and protein structure, known as the protein folding problem, needs to be elucidated. The protein folding problem remains elusive, with significant difficulties still arising when modeling amino acid sequences lacking an identifiable template. Understanding protein folding will allow for unforeseen advances in protein design; often referred to as the inverse protein folding problem. Despite challenges in protein folding, de novo protein design has recently demonstrated significant success via computational techniques. We review advances and challenges in protein structure prediction and de novo protein design, and highlight their interplay in successful biotechnological applications

Forcefield_NCAA: Ab Initio Charge Parameters to Aid in the Discovery and Design of Therapeutic Proteins and Peptides with Unnatural Amino Acids and Their Application to Complement Inhibitors of the Compstatin Family

We describe the development and testing of ab initio derived, AMBER ff03 compatible charge parameters for a large library of 147 noncanonical amino acids including β- and N-methylated amino acids for use in applications such as protein structure prediction and de novo protein design. The charge parameter derivation was performed using the RESP fitting approach. Studies were performed assessing the suitability of the derived charge parameters in discriminating the activity/inactivity between 63 analogs of the complement inhibitor Compstatin on the basis of previously published experimental IC50 data and a screening procedure involving short simulations and binding free energy calculations. We found that both the approximate binding affinity (K*) and the binding free energy calculated through MM-GBSA are capable of discriminating between active and inactive Compstatin analogs, with MM-GBSA performing significantly better. Key interactions between the most potent Compstatin analog that contains a noncanonical amino acid are presented and compared to the most potent analog containing only natural amino acids and native Compstatin. We make the derived parameters and an associated web interface that is capable of performing modifications on proteins using Forcefield_NCAA and outputting AMBER-ready topology and parameter files freely available for academic use at http://selene.princeton.edu/FFNCAA. The forcefield allows one to incorporate these customized amino acids into design applications with control over size, van der Waals, and electrostatic interactions

Protein WISDOM: A Workbench for <em>In silico</em> <em>De novo</em> Design of BioMolecules

The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity. To disseminate these methods for broader use we present Protein WISDOM (http://www.proteinwisdom.org), a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity. To disseminate these methods for broader use we present Protein WISDOM (http://www.proteinwisdom.org), a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods

conSSert: Consensus SVM Model for Accurate Prediction of Ordered Secondary Structure

Accurate prediction of protein secondary structure remains a crucial step in most approaches to the protein-folding problem, yet the prediction of ordered secondary structure, specifically beta-strands, remains a challenge. We developed a consensus secondary structure prediction method, conSSert, which is based on support vector machines (SVM) and provides exceptional accuracy for the prediction of beta-strands with QE accuracy of over 0.82 and a Q2-EH of 0.86. conSSert uses as input probabilities for the three types of secondary structure (helix, strand, and coil) that are predicted by four top performing methods: PSSpred, PSIPRED, SPINE-X, and RAPTOR. conSSert was trained/tested using 4261 protein chains from PDBSelect25, and 8632 chains from PISCES. Further validation was performed using targets from CASP9, CASP10, and CASP11. Our data suggest that poor performance in strand prediction is likely a result of training bias and not solely due to the nonlocal nature of beta-sheet contacts. conSSert is freely available for noncommercial use as a webservice: http://ares.tamu.edu/conSSert/

Forcefield_PTM: <i>Ab Initio</i> Charge and AMBER Forcefield Parameters for Frequently Occurring Post-Translational Modifications

In this work, we introduce Forcefield_PTM, a set of AMBER forcefield parameters consistent with ff03 for 32 common post-translational modifications. Partial charges were calculated through <i>ab initio</i> calculations and a two-stage RESP-fitting procedure in an ether-like implicit solvent environment. The charges were found to be generally consistent with others previously reported for phosphorylated amino acids, and trimethyllysine, using different parametrization methods. Pairs of modified structures and their corresponding unmodified structures were curated from the PDB for both single and multiple modifications. Background structural similarity was assessed in the context of secondary and tertiary structures from the global data set. Next, the charges derived for Forcefield_PTM were tested on a macroscopic scale using unrestrained all-atom Langevin molecular dynamics simulations in AMBER for 34 (17 pairs of modified/unmodified) systems in implicit solvent. Assessment was performed in the context of secondary structure preservation, stability in energies, and correlations between the modified and unmodified structure trajectories on the aggregate. As an illustration of their utility, the parameters were used to compare the structural stability of the phosphorylated and dephosphorylated forms of OdhI. Microscopic comparisons between quantum and AMBER single point energies along key χ torsions on several PTMs were performed, and corrections to improve their agreement in terms of mean-squared errors and squared correlation coefficients were parametrized. This forcefield for post-translational modifications in condensed-phase simulations can be applied to a number of biologically relevant and timely applications including protein structure prediction, protein and peptide design, and docking and to study the effect of PTMs on folding and dynamics. We make the derived parameters and an associated interactive webtool capable of performing post-translational modifications on proteins using Forcefield_PTM available at http://selene.princeton.edu/FFPTM