Prediction of structures of zinc‐binding proteins through explicit modeling of metal coordination geometry

Metal ions play an essential role in stabilizing protein structures and contributing to protein function. Ions such as zinc have well‐defined coordination geometries, but it has not been easy to take advantage of this knowledge in protein structure prediction efforts. Here, we present a computational method to predict structures of zinc‐binding proteins given knowledge of the positions of zinc‐coordinating residues in the amino acid sequence. The method takes advantage of the “atom‐tree” representation of molecular systems and modular architecture of the Rosetta3 software suite to incorporate explicit metal ion coordination geometry into previously developed de novo prediction and loop modeling protocols. Zinc cofactors are tethered to their interacting residues based on coordination geometries observed in natural zinc‐binding proteins. The incorporation of explicit zinc atoms and their coordination geometry in both de novo structure prediction and loop modeling significantly improves sampling near the native conformation. The method can be readily extended to predict protein structures bound to other metal and/or small chemical cofactors with well‐defined coordination or ligation geometry.     

Improving prediction of helix–helix packing in membrane proteins using predicted contact numbers as restraints

One of the challenging problems in tertiary structure prediction of helical membrane proteins (HMPs) is the determination of rotation of α‐helices around the helix normal. Incorrect prediction of helix rotations substantially disrupts native residue–residue contacts while inducing only a relatively small effect on the overall fold. We previously developed a method for predicting residue contact numbers (CNs), which measure the local packing density of residues within the protein tertiary structure. In this study, we tested the idea of incorporating predicted CNs as restraints to guide the sampling of helix rotation. For a benchmark set of 15 HMPs with simple to rather complicated folds, the average contact recovery (CR) of best‐sampled models was improved for all targets, the likelihood of sampling models with CR greater than 20% was increased for 13 targets, and the average RMSD100 of best‐sampled models was improved for 12 targets. This study demonstrated that explicit incorporation of CNs as restraints improves the prediction of helix–helix packing. Proteins 2017; 85:1212–1221. © 2017 Wiley Periodicals, Inc.     

Selection and structural analysis of de novo proteins from an α3β3 genetic library

The construction of novel functional proteins has been a key area of protein engineering. However, there are few reports of functional proteins constructed from artificial scaffolds. Here, we have constructed a genetic library encoding α3β3 de novo proteins to generate novel scaffolds in smaller size using a binary combination of simplified hydrophobic and hydrophilic amino acid sets. To screen for folded de novo proteins, we used a GFP‐based screening system and successfully obtained the proteins from the colonies emitting the very bright fluorescence as a similar intensity of GFP. Proteins isolated from the very bright colonies (vTAJ) and bright colonies (wTAJ) were analyzed by circular dichroism (CD), 8‐anilino‐1‐naphthalenesulfonate (ANS) binding assay, and analytical size‐exclusion chromatography (SEC). CD studies revealed that vTAJ and wTAJ proteins had both α‐helix and β‐sheet structures with thermal stabilities. Moreover, the selected proteins demonstrated a variety of association states existing as monomer, dimer, and oligomer formation. The SEC and ANS binding assays revealed that vTAJ proteins tend to be a characteristic of the folded protein, but not in a molten‐globule state. A vTAJ protein, vTAJ13, which has a packed globular structure and exists as a monomer, was further analyzed by nuclear magnetic resonance. NOE connectivities between backbone signals of vTAJ13 suggested that the protein contains three α‐helices and three β‐strands as intended by its design. Thus, it would appear that artificially generated α3β3 de novo proteins isolated from very bright colonies using the GFP fusion system exhibit excellent properties similar to folded proteins and would be available as artificial scaffolds to generate functional proteins with catalytic and ligand binding properties.     

Computational modeling of membrane proteins

The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1–2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug‐specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans‐membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α‐helical MPs as well as β‐barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge‐based scoring functions. Moreover, de novo methods have benefited from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade. Proteins 2015; 83:1–24. © 2014 Wiley Periodicals, Inc.     

Rational design of amyloid β peptide–binding proteins: Pseudo‐Aβ β‐sheet surface presented in green fluorescent protein binds tightly and preferentially to structured Aβ

Some neurodegenerative diseases such as Alzheimer disease (AD) and Parkinson disease are caused by protein misfolding. In AD, amyloid β‐peptide (Aβ) is thought to be a toxic agent by self‐assembling into a variety of aggregates involving soluble oligomeric intermediates and amyloid fibrils. Here, we have designed several green fluorescent protein (GFP) variants that contain pseudo‐Aβ β‐sheet surfaces and evaluated their abilities to bind to Aβ and inhibit Aβ oligomerization. Two GFP variants P13H and AP93Q bound tightly to Aβ, K_d = 260 nM and K_d = 420 nM, respectively. Moreover, P13H and AP93Q were capable of efficiently suppressing the generation of toxic Aβ oligomers as shown by a cell viability assay. By combining the P13H and AP93Q mutations, a super variant SFAB4 comprising four strands of Aβ‐derived sequences was designed and bound more tightly to Aβ (K_d = 100 nM) than those having only two pseudo‐Aβ strands. The SFAB4 protein preferentially recognized the soluble oligomeric intermediates of Aβ more than both unstructured monomer and mature amyloid fibrils. Thus, the design strategy for embedding pseudo‐Aβ β‐sheet structures onto a protein surface arranged in the β‐barrel structure is useful to construct molecules capable of binding tightly to Aβ and inhibiting its aggregation. This strategy may provide implication for the diagnostic and therapeutic development in the treatment of AD. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

A Continuum theory for the prediction of lateral and rotational positioning of α-helices in membrane proteins: Bacteriorhodopsin

We have developed a new method for the prediction of the lateral and the rotational positioning of transmembrane helices, based upon the present status of knowledge about the dominant interaction of the tertiary structure formation. The basic assumption about the interaction is that the interhelix binding is due to the polar interactions and that very short extramembrane loop segments restrict the relative position of the helices. Another assumption is made for the simplification of the prediction that a helix may be regarded as a continuum rod having polar interaction fields around it. The polar interaction field is calculated by a probe helix method, using a copolymer of serine and alanine as probe helices. The lateral position of helices is determined by the strength of the interhelix binding estimated from the polar interaction field together with the length of linking loop segments. The rotational positioning is determined by the polar interaction field, assuming the optimum lateral configuration. The structural change due to the binding of a prosthetic group is calculated, fixing the rotational freedom of a helix that is connected to the prosthetic group. Applying this method to bacteriorhodopsin, the optimum lateral and rotational positioning of transmembrane helices that are very similar to the experimental configuration was obtained. This method was implemented by a software system, which was developed for this work, and automatic calculation became possible for membrane proteins comprised of several transmembrane helices. © 1995 Wiley-Liss, Inc.     

Quantitative prediction of protein–protein binding affinity with a potential of mean force considering volume correction

Quantitative prediction of protein–protein binding affinity is essential for understanding protein–protein interactions. In this article, an atomic level potential of mean force (PMF) considering volume correction is presented for the prediction of protein–protein binding affinity. The potential is obtained by statistically analyzing X‐ray structures of protein–protein complexes in the Protein Data Bank. This approach circumvents the complicated steps of the volume correction process and is very easy to implement in practice. It can obtain more reasonable pair potential compared with traditional PMF and shows a classic picture of nonbonded atom pair interaction as Lennard‐Jones potential. To evaluate the prediction ability for protein–protein binding affinity, six test sets are examined. Sets 1–5 were used as test set in five published studies, respectively, and set 6 was the union set of sets 1–5, with a total of 86 protein–protein complexes. The correlation coefficient (R) and standard deviation (SD) of fitting predicted affinity to experimental data were calculated to compare the performance of ours with that in literature. Our predictions on sets 1–5 were as good as the best prediction reported in the published studies, and for union set 6, R = 0.76, SD = 2.24 kcal/mol. Furthermore, we found that the volume correction can significantly improve the prediction ability. This approach can also promote the research on docking and protein structure prediction.     

Towards meeting the paracelsus challenge: The design,synthesis, and characterization of paracelsin-43, an α-helical protein with over 50% sequence identity to an all-β protein

In response to the Paracelsus Challenge (Rose and Creamer, Proteins, 19:1–3, 1994), we present here the design, synthesis, and characterization of a helical protein, whose sequence is 50% identical to that of an all-β protein. The new sequence was derived by applying an inverse protein folding approach, in which the sequence was optimized to “fit” the new helical structure, but constrained to retain 50% of the original amino acid residues. The program utilizes a genetic algorithm to optimize the sequence, together with empirical potentials of mean force to evaluate the sequence-structure compatibility. Although the designed sequence has little ordered (secondary) structure in water, circular dichroism and nuclear magnetic resonance data show clear evidence for significant helical content in water/ethylene glycol and in water/methanol mixtures at low temperatures, as well as melting behavior indicative of cooperative folding. We believe that this represents a significant step toward meeting the Paracelsus Challenge.     

Absolute binding free energy calculations: On the accuracy of computational scoring of protein–ligand interactions

Calculating the absolute binding free energies is a challenging task. Reliable estimates of binding free energies should provide a guide for rational drug design. It should also provide us with deeper understanding of the correlation between protein structure and its function. Further applications may include identifying novel molecular scaffolds and optimizing lead compounds in computer‐aided drug design. Available options to evaluate the absolute binding free energies range from the rigorous but expensive free energy perturbation to the microscopic linear response approximation (LRA/β version) and related approaches including the linear interaction energy (LIE) to the more approximated and considerably faster scaled protein dipoles Langevin dipoles (PDLD/S‐LRA version) as well as the less rigorous molecular mechanics Poisson–Boltzmann/surface area (MM/PBSA) and generalized born/surface area (MM/GBSA) to the less accurate scoring functions. There is a need for an assessment of the performance of different approaches in terms of computer time and reliability. We present a comparative study of the LRA/β, the LIE, the PDLD/S‐LRA/β, and the more widely used MM/PBSA and assess their abilities to estimate the absolute binding energies. The LRA and LIE methods perform reasonably well but require specialized parameterization for the nonelectrostatic term. The PDLD/S‐LRA/β performs effectively without the need of reparameterization. Our assessment of the MM/PBSA is less optimistic. This approach appears to provide erroneous estimates of the absolute binding energies because of its incorrect entropies and the problematic treatment of electrostatic energies. Overall, the PDLD/S‐LRA/β appears to offer an appealing option for the final stages of massive screening approaches. Proteins 2010. © 2010 Wiley‐Liss, Inc.