René Staritzbichler

Solvent accessible surface area approximations for rapid and accurate protein structure prediction

The burial of hydrophobic amino acids in the protein core is a driving force in protein folding. The extent to which an amino acid interacts with the solvent and the protein core is naturally proportional to the surface area exposed to these environments. However, an accurate calculation of the solvent-accessible surface area (SASA), a geometric measure of this exposure, is numerically demanding as it is not pair-wise decomposable. Furthermore, it depends on a full-atom representation of the molecule. This manuscript introduces a series of four SASA approximations of increasing computational complexity and accuracy as well as knowledge-based environment free energy potentials based on these SASA approximations. Their ability to distinguish correctly from incorrectly folded protein models is assessed to balance speed and accuracy for protein structure prediction. We find the newly developed “Neighbor Vector” algorithm provides the most optimal balance of accurate yet rapid exposure measures.

A Study of the Evolution of Inverted-Topology Repeats from LeuT-Fold Transporters Using AlignMe

X-ray crystal structures have revealed that numerous secondary transporter proteins originally categorized into different sequence families share similar structures, namely, the LeuT fold. The core of this fold consists of two units of five transmembrane helices, whose conformations have been proposed to exchange to form the two alternate states required for transport. That these two units are related implies that LeuT-like transporters evolved from gene-duplication and fusion events. Thus, the origins of this structural repeat may be relevant to the evolution of transport function. However, the lack of significant sequence similarity requires sensitive sequence search methods for analyzing their evolution. To this end, we developed a software application called AlignMe, which can use various types of input information, such as residue hydrophobicity, to perform pairwise alignments of sequences and/or of hydropathy profiles of (membrane) proteins. We used AlignMe to analyze the evolutionary relationships between repeats of the LeuT fold. In addition, we identified proteins from the so-called DedA family that potentially share a common ancestor with these repeats. DedA domains have been implicated in, e.g., selenite uptake; they are found widely distributed across all kingdoms of life; two or more DedA domains are typically found per genome, and some may adopt dual topologies. These results suggest that DedA proteins existed in ancient organisms and may function as dimers, as required for a would-be ancestor of the LeuT fold. In conclusion, we provide novel insights into the evolution of this important structural motif and thus potentially into the alternating-access mechanism of transport itself.

A unified hydrophobicity scale for multispan membrane proteins

The concept of hydrophobicity is critical to our understanding of the principles of membrane protein (MP) folding, structure, and function. In the last decades, several groups have derived hydrophobicity scales using both experimental and statistical methods that are optimized to mimic certain natural phenomena as closely as possible. The present work adds to this toolset the first knowledge‐based scale that unifies the characteristics of both α‐helical and β‐barrel multispan MPs. This unified hydrophobicity scale (UHS) distinguishes between amino acid preference for solution, transition, and trans‐membrane states. The scale represents average hydrophobicity values of amino acids in folded proteins, irrespective of their secondary structure type. We furthermore present the first knowledge‐based hydrophobicity scale for mammalian α‐helical MPs (mammalian hydrophobicity scale—MHS). Both scales are particularly useful for computational protein structure elucidation, for example as input for machine learning techniques, such as secondary structure or trans‐membrane span prediction, or as reference energies for protein structure prediction or protein design. The knowledge‐based UHS shows a striking similarity to a recent experimental hydrophobicity scale introduced by Hessa and coworkers (Hessa T et al., Nature 2007;450:U1026–U1032). Convergence of two very different approaches onto similar hydrophobicity values consolidates the major differences between experimental and knowledge‐based scales observed in earlier studies. Moreover, the UHS scale represents an accurate absolute free energy measure for folded, multispan MPs—a feature that is absent from many existing scales. The utility of the UHS was demonstrated by analyzing a series of diverse MPs. It is further shown that the UHS outperforms nine established hydrophobicity scales in predicting trans‐membrane spans along the protein sequence. The accuracy of the present hydrophobicity scale profits from the doubling of the number of integral MPs in the PDB over the past four years. The UHS paves the way for an increased accuracy in the prediction of trans‐membrane spans. Proteins 2009.

Alignment of helical membrane protein sequences using AlignMe.

Few sequence alignment methods have been designed specifically for integral membrane proteins, even though these important proteins have distinct evolutionary and structural properties that might affect their alignments. Existing approaches typically consider membrane-related information either by using membrane-specific substitution matrices or by assigning distinct penalties for gap creation in transmembrane and non-transmembrane regions. Here, we ask whether favoring matching of predicted transmembrane segments within a standard dynamic programming algorithm can improve the accuracy of pairwise membrane protein sequence alignments. We tested various strategies using a specifically designed program called AlignMe. An updated set of homologous membrane protein structures, called HOMEP2, was used as a reference for optimizing the gap penalties. The best of the membrane-protein optimized approaches were then tested on an independent reference set of membrane protein sequence alignments from the BAliBASE collection. When secondary structure (S) matching was combined with evolutionary information (using a position-specific substitution matrix (P)), in an approach we called AlignMePS, the resultant pairwise alignments were typically among the most accurate over a broad range of sequence similarities when compared to available methods. Matching transmembrane predictions (T), in addition to evolutionary information, and secondary-structure predictions, in an approach called AlignMePST, generally reduces the accuracy of the alignments of closely-related proteins in the BAliBASE set relative to AlignMePS, but may be useful in cases of extremely distantly related proteins for which sequence information is less informative. The open source AlignMe code is available at https://sourceforge.net/projects/alignme/, and at http://www.forrestlab.org, along with an online server and the HOMEP2 data set.

BCL::Fold - De Novo Prediction of Complex and Large Protein Topologies by Assembly of Secondary Structure Elements

Computational de novo protein structure prediction is limited to small proteins of simple topology. The present work explores an approach to extend beyond the current limitations through assembling protein topologies from idealized α-helices and β-strands. The algorithm performs a Monte Carlo Metropolis simulated annealing folding simulation. It optimizes a knowledge-based potential that analyzes radius of gyration, β-strand pairing, secondary structure element (SSE) packing, amino acid pair distance, amino acid environment, contact order, secondary structure prediction agreement and loop closure. Discontinuation of the protein chain favors sampling of non-local contacts and thereby creation of complex protein topologies. The folding simulation is accelerated through exclusion of flexible loop regions further reducing the size of the conformational search space. The algorithm is benchmarked on 66 proteins with lengths between 83 and 293 amino acids. For 61 out of these proteins, the best SSE-only models obtained have an RMSD100 below 8.0 Å and recover more than 20% of the native contacts. The algorithm assembles protein topologies with up to 215 residues and a relative contact order of 0.46. The method is tailored to be used in conjunction with low-resolution or sparse experimental data sets which often provide restraints for regions of defined secondary structure.

AlignMe—a membrane protein sequence alignment web server

We present a web server for pair-wise alignment of membrane protein sequences, using the program AlignMe. The server makes available two operational modes of AlignMe: (i) sequence to sequence alignment, taking two sequences in fasta format as input, combining information about each sequence from multiple sources and producing a pair-wise alignment (PW mode); and (ii) alignment of two multiple sequence alignments to create family-averaged hydropathy profile alignments (HP mode). For the PW sequence alignment mode, four different optimized parameter sets are provided, each suited to pairs of sequences with a specific similarity level. These settings utilize different types of inputs: (position-specific) substitution matrices, secondary structure predictions and transmembrane propensities from transmembrane predictions or hydrophobicity scales. In the second (HP) mode, each input multiple sequence alignment is converted into a hydrophobicity profile averaged over the provided set of sequence homologs; the two profiles are then aligned. The HP mode enables qualitative comparison of transmembrane topologies (and therefore potentially of 3D folds) of two membrane proteins, which can be useful if the proteins have low sequence similarity. In summary, the AlignMe web server provides user-friendly access to a set of tools for analysis and comparison of membrane protein sequences. Access is available at http://www.bioinfo.mpg.de/AlignMe

BCL::Score--knowledge based energy potentials for ranking protein models represented by idealized secondary structure elements.

The topology of most experimentally determined protein domains is defined by the relative arrangement of secondary structure elements, i.e. α-helices and β-strands, which make up 50–70% of the sequence. Pairing of β-strands defines the topology of β-sheets. The packing of side chains between α-helices and β-sheets defines the majority of the protein core. Often, limited experimental datasets restrain the position of secondary structure elements while lacking detail with respect to loop or side chain conformation. At the same time the regular structure and reduced flexibility of secondary structure elements make these interactions more predictable when compared to flexible loops and side chains. To determine the topology of the protein in such settings, we introduce a tailored knowledge-based energy function that evaluates arrangement of secondary structure elements only. Based on the amino acid Cβ atom coordinates within secondary structure elements, potentials for amino acid pair distance, amino acid environment, secondary structure element packing, β-strand pairing, loop length, radius of gyration, contact order and secondary structure prediction agreement are defined. Separate penalty functions exclude conformations with clashes between amino acids or secondary structure elements and loops that cannot be closed. Each individual term discriminates for native-like protein structures. The composite potential significantly enriches for native-like models in three different databases of 10,000–12,000 protein models in 80–94% of the cases. The corresponding application, “BCL::ScoreProtein,” is available at www.meilerlab.org.

Novel scoring function for modeling structures of oligomers of transmembrane α‐helices

Specific non‐covalent interactions between transmembrane (TM) α‐helices are important in a variety of biological processes. Experimental and computational studies have shown that van der Waals interactions play an important role in the tight packing between TM α‐helices, although polar interactions can also be important in some instances. Based on the assumption that van der Waals interaction alone is sufficient for a meso‐scale (residue‐scale) description of the interaction between TM α‐helices, we have designed a novel residue‐scale scoring function for modeling structures of oligomers of TM α‐helices. We first calculated atomistic van der Waals interaction energies between two amino acids, X and Y, of a pair of parallel α‐helices, glycine‐X‐glycine and glycine‐Y‐glycine and compiled them according to three variables, the distance between the two Cα atoms and the rotational angles of X and Y about their helical axes. Upon averaging over the rotational angles, we obtained one‐dimensional interaction energy profiles that are functions of the distance between Cα atoms only. Each of the interaction energy profiles was fitted with a generic fitting function of the distance between Cα atoms, yielding analytical scoring functions for all possible amino acid pairs. For glycophorin A, neu/erbB‐2, and phospholamban, lowest‐energy conformations obtained through exhaustive scanning of the entire conformational space using the scoring functions were compatible with available experimental data. Proteins 2004.

Automated Protein-Insertion into Membranes for Molecular Dynamics Simulation Set-Up Using Taragrid

Given a known membrane protein structure, a crucial and non-trivial preparation step in order to perform simulations of the protein in a lipid bilayer is the creation of the equilibrated bilayer-protein system. TaraGrid links an implicit protein force field with standard MD packages to automate this process. In the initial steps TaraGrid places the protein into the membrane and carves any water molecules out of the protein volume. It also erases as many lipids as necessary to conserve the bilayer density. In the main optimization phase TaraGrid calculates intermolecular forces between the protein and the molecules of the bilayer-solution system. Molecules that are within the protein volume are assigned a force that pushes them out of that volume. Molecules outside of the protein surface are assigned a linear combination of electrostatic and van-der-Waals forces. These forces are passed to a subsequent MD step carried out with a standard MD package, to obtain new peptide and water positions. This procedure enables creation of realistic and reproducible starting conformations for membrane-protein simulations within a reasonable time and with minimal intervention. Presently TaraGrid is tested to interact with NAMD and GROMACS, but as a standalone tool it is designed to work with any existing MD package.

Automated and Optimized Embedding of Proteins into Membranes for Molecular Dynamics Simulations using Griffin

As new atomic structures of membrane proteins are resolved, they reveal increasingly complex transmembrane topologies, and often highly irregular surfaces with crevices and pores. In many cases, specific interactions with the lipid membrane are formed and are functionally crucial, as is the overall lipid composition. Compounded with increasing protein size, these characteristics pose a challenge for the construction of high-quality simulation models of membrane proteins in lipid bilayers; that these models are sufficiently realistic is of obvious importance for the reliability of simulation-based studies of these systems. To automate and optimize this process, we have developed GRIFFIN (GRId-based Force-Field INput). In the initial steps of this embedding protocol, the program carves lipid and water molecules out of the protein volume as necessary to conserve the system density. In the main optimization phase GRIFFIN adds an implicit, grid-based protein force field to the molecular simulation of the carved membrane-water system. In this force field, molecules inside the implicit protein volume experience an outward force that will expel them from that volume, whereas molecules outside are subject to electrostatic and van-der-Waals attractive interactions with the implicit protein. At each step of the simulation, these are updated by GRIFFIN and combined with the intermolecular forces of the explicit membrane-water system, to derive a trajectory of the atomic positions. This procedure enables the construction of realistic and reproducible starting configurations of the protein-membrane interface within a reasonable timeframe and with minimal intervention. GRIFFIN is a standalone tool it is designed to work with any existing molecular dynamics package, such as NAMD or GROMACS. Examples of challenging applications are presented.