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

Characterization of the Hemagglutinin Cleaving Transmembrane Serine Proteases Matriptase and TMPRSS2

Influenza is one of the commonest infectious diseases affecting millions of people every year including 290,000 – 650,000 heavy casualties. Influenza viruses undergo constant genetic changes and every 10 – 50 years new influenza virus strains emerge that potentially cause a severe pandemic. In this modern interconnected world, experts believe the next influenza pandemic will be a “devastating global health event with far-reaching consequences” [1]. Novel effective anti-influenza drugs are in need. One strategy of influenza research is to focus on host-specific proteases that are essential for virus activation and spread. Trypsin-like serine proteases are crucial for influenza activation by mediating the cleavage of the viral surface glycoprotein HA and hence promoting the fusion potential of the virus. Therefore, their inhibition provides a promising therapeutic approach. The present work focused on the characterization of two relevant HA cleaving type-II transmembrane serine proteases matriptase and TMPRSS2. Chapter 3 and chapter 4 of this thesis engaged with the recombinant production of matriptase (chapter 3) in order to obtain a pure functional enzyme of high quality for a SAR study with novel monobasic (hence potentially bioavailable) matriptase inhibitors of the 3-amidinophenylalanine type (chapter 4). Adequate amounts of high-quality matriptase enzymes were isolated using a new expression system and in total 5 matriptase crystals were available at the end of this thesis for structural analysis. The matriptase inhibitor design in this thesis focused on matriptase-affine compounds with a fair selectivity profile against the blood coagulation enzymes thrombin and fXa. In total, 18 new monobasic and potentially bioavailable, as well as four new dibasic compounds of the 3-amidinophenylalanine types were tested. Based on the last published crystal structure of this inhibitor type in complex with matriptase from 2006 (PDB code 2GV6) docking was used as a structure-based virtual screening method for lead optimization of the compounds N-terminus. Selected compounds were suggested to interact with the carbonyl side chain of Gln175 of matriptase to achieve a higher affinity of matriptase compared to fXa. The 4-tert-butylureido-piperidine could be identified as suitable C-terminus in combination with 3-fluoro-4-hydroxymethyl biphenylsulphonyl N-terminally in order to obtain excellent selectivity over thrombin. The binding mode of this compound (compound 55) was crystallographically determined in complex with matriptase as well as trypsin. Trypsin proved as a suitable alternative to matriptase for detailed binding mode analysis of the compounds N-terminus. However, different preferences were detected for the C-terminus. Dibasic compounds showed higher matriptase affinity and selectivity in comparison with the monobasic analogues. However, the tested monobasic compounds were still decent matriptase inhibitors that are additionally suitable for cell culture and animal studies in their benzamidine prodrug forms, which are well established from related inhibitors of thrombin. In addition, selected monobasic as well as dibasic compounds demonstrated strong suppression of the replication of certain H9N2 influenza viruses in a matriptase-expressing MDCK II cell model. These matriptase inhibitors could be potential lead structures for the development of new drugs against H9 strains for influenza. TMPRSS2 is widely discussed for its role in influenza activation. With a TMPRSS2 dependancy of HA-activation of certain subtypes, the characterization of this protease is an important prerequisite for being available as a target for influenza drug design. However, only little is known about the physiological function of TMPRSS2 and no experimental structure data are available at the moment to enable a structure-based drug development. Therefore, chapter 5 of this thesis focused on the characterization of TMPRSS2 in order to develop a strategy for the isolation of proteolytically active TMPRSS2 from cell culture. Even though, no functional TMPRSS2 could be recovered at the end of this work some new structural characteristics of TMPRSS2 were identified as crucial for functionality insight the cell. In general, TMPRSS2 without the cytosolic part, the transmembrane domain and the LDLRA domain is able to undergo autocatalytically activation if an artificial signal peptide was added N-terminal to enable entry into the endoplasmic reticulum. The presence of the cysteine-rich SRCR domain and the presence of the disulfide chain that connects the SPD and the stem region after activation cleavage have been identified as crucial for activity. N-terminal truncation of TMPRSS2 did not result in obvious dislocation within the cell: as the full-length positive control truncated TMPRSS2 was exclusively found in cell compartments surrounding the nucleus in immunofluorescence experiments. However, a reduced proteolytic cleavage activity towards H3-HA in co-expression experiments has been observed and might be a result of dislocation, since truncated TMPRSS2 is not bound to the biomembrane anymore. In addition, TMPRSS2 has been identified as a potential substrate of matriptase in vitro, which suggests possible participation in several zymogen cascades

Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

Practically Useful: What the Rosetta Protein Modeling Suite Can Do for You

The objective of this review is to enable researchers to use the software package ROSETTA for biochemical and biomedicinal studies. We provide a brief review of the six most frequent research problems tackled with ROSETTA. For each of these six tasks, we provide a tutorial that illustrates a basic ROSETTA protocol. The ROSETTA method was originally developed for de novo protein structure prediction and is regularly one of the best performers in the community-wide biennial Critical Assessment of Structure Prediction. Predictions for protein domains with fewer than 125 amino acids regularly have a backbone root-mean-square deviation of better than 5.0 A ˚. More impressively, there are several cases in which ROSETTA has been used to predict structures with atomic level accuracy better than 2.5 A ˚. In addition to de novo structure prediction, ROSETTA also has methods for molecular docking, homology modeling, determining protein structures from sparse experimental NMR or EPR data, and protein design. ROSETTA has been used to accurately design a novel protein structure, predict the structure of protein-protein complexes, design altered specificity protein-protein and protein-DNA interactions, and stabilize proteins and protein complexes. Most recently, ROSETTA has been used to solve the X-ray crystallographic phase problem. ROSETTA is a unified software package for protein structure prediction and functional design. It has been used to predic

Step-by-step design of proteins for small molecule interaction: a review on recent milestones

Protein design is the field of synthetic biology that aims at developing de-novo custom made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the art software is briefly explored.publishe

Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

Protein-protein interactions: impact of solvent and effects of fluorination

Proteins have an indispensable role in the cell. They carry out a wide variety of structural, catalytic and signaling functions in all known biological systems. To perform their biological functions, proteins establish interactions with other bioorganic molecules including other proteins. Therefore, protein-protein interactions is one of the central topics in molecular biology. My thesis is devoted to three different topics in the field of protein-protein interactions. The first one focuses on solvent contribution to protein interfaces as it is an important component of protein complexes. The second topic discloses the structural and functional potential of fluorine's unique properties, which are attractive for protein design and engineering not feasible within the scope of canonical amino acids. The last part of this thesis is a study of the impact of charged amino acid residues within the hydrophobic interface of a coiled-coil system, which is one of the well-established model systems for protein-protein interactions studies. I. The majority of proteins interact in vivo in solution, thus studies of solvent impact on protein-protein interactions could be crucial for understanding many processes in the cell. However, though solvent is known to be very important for protein-protein interactions in terms of structure, dynamics and energetics, its effects are often disregarded in computational studies because a detailed solvent description requires complex and computationally demanding approaches. As a consequence, many protein residues, which establish water-mediated interactions, are neither considered in an interface definition. In the previous work carried out in our group the protein interfaces database (SCOWLP) has been developed. This database takes into account interfacial solvent and based on this classifies all interfacial protein residues of the PDB into three classes based on their interacting properties: dry (direct interaction), dual (direct and water-mediated interactions), and wet spots (residues interacting only through one water molecule). To define an interaction SCOWLP considers a donor–acceptor distance for hydrogen bonds of 3.2 Å, for salt bridges of 4 Å, and for van der Waals contacts the sum of the van der Waals radii of the interacting atoms. In previous studies of the group, statistical analysis of a non-redundant protein structure dataset showed that 40.1% of the interfacial residues participate in water-mediated interactions, and that 14.5% of the total residues in interfaces are wet spots. Moreover, wet spots have been shown to display similar characteristics to residues contacting water molecules in cores or cavities of proteins. The goals of this part of the thesis were: 1. to characterize the impact of solvent in protein-protein interactions 2. to elucidate possible effects of solvent inclusion into the correlated mutations approach for protein contacts prediction To study solvent impact on protein interfaces a molecular dynamics (MD) approach has been used. This part of the work is elaborated in section 2.1 of this thesis. We have characterized properties of water-mediated protein interactions at residue and solvent level. For this purpose, an MD analysis of 17 representative complexes from SH3 and immunoglobulin protein families has been performed. We have shown that the interfacial residues interacting through a single water molecule (wet spots) are energetically and dynamically very similar to other interfacial residues. At the same time, water molecules mediating protein interactions have been found to be significantly less mobile than surface solvent in terms of residence time. Calculated free energies indicate that these water molecules should significantly affect formation and stability of a protein-protein complex. The results obtained in this part of the work also suggest that water molecules in protein interfaces contribute to the conservation of protein interactions by allowing more sequence variability in the interacting partners, which has important implications for the use of the correlated mutations concept in protein interactions studies. This concept is based on the assumption that interacting protein residues co-evolve, so that a mutation in one of the interacting counterparts is compensated by a mutation in the other. The study presented in section 2.2 has been carried out to prove that an explicit introduction of solvent into the correlated mutations concept indeed yields qualitative improvement of existing approaches. For this, we have used the data on interfacial solvent obtained from the SCOWLP database (the whole PDB) to construct a “wet” similarity matrix. This matrix has been used for prediction of protein contacts together with a well-established “dry” matrix. We have analyzed two datasets containing 50 domains and 10 domain pairs, and have compared the results obtained by using several combinations of both “dry” and “wet” matrices. We have found that for predictions for both intra- and interdomain contacts the introduction of a combination of a “dry” and a “wet” similarity matrix improves the predictions in comparison to the “dry” one alone. Our analysis opens up the idea that the consideration of water may have an impact on the improvement of the contact predictions obtained by correlated mutations approaches. There are two principally novel aspects in this study in the context of the used correlated mutations methodology : i) the first introduction of solvent explicitly into the correlated mutations approach; ii) the use of the definition of protein-protein interfaces, which is essentially different from many other works in the field because of taking into account physico-chemical properties of amino acids and not being exclusively based on distance cut-offs. II. The second part of the thesis is focused on properties of fluorinated amino acids in protein environments. In general, non-canonical amino acids with newly designed side-chain functionalities are powerful tools that can be used to improve structural, catalytic, kinetic and thermodynamic properties of peptides and proteins, which otherwise are not feasible within the use of canonical amino acids. In this context fluorinated amino acids have increasingly gained in importance in protein chemistry because of fluorine's unique properties: high electronegativity and a small atomic size. Despite the wide use of fluorine in drug design, properties of fluorine in protein environments have not been yet extensively studied. The aims of this part of the dissertation were: 1. to analyze the basic properties of fluorinated amino acids such as electrostatic and geometric characteristics, hydrogen bonding abilities, hydration properties and conformational preferences (section 3.1) 2. to describe the behavior of fluorinated amino acids in systems emulating protein environments (section 3.2, section 3.3) First, to characterize fluorinated amino acids side chains we have used fluorinated ethane derivatives as their simplified models and applied a quantum mechanics approach. Properties such as charge distribution, dipole moments, volumes and size of the fluoromethylated groups within the model have been characterized. Hydrogen bonding properties of these groups have been compared with the groups typically presented in natural protein environments. We have shown that hydrogen and fluorine atoms within these fluoromethylated groups are weak hydrogen bond donors and acceptors. Nevertheless they should not be disregarded for applications in protein engineering. Then, we have implemented four fluorinated L-amino acids for the AMBER force field and characterized their conformational and hydration properties at the MD level. We have found that hydrophobicity of fluorinated side chains grows with the number of fluorine atoms and could be explained in terms of high electronegativity of fluorine atoms and spacial demand of fluorinated side-chains. These data on hydration agrees with the results obtained in the experimental work performed by our collaborators. We have rationally engineered systems that allow us to study fluorine properties and extract results that could be extrapolated to proteins. For this, we have emulated protein environments by introducing fluorinated amino acids into a parallel coiled-coil and enzyme-ligand chymotrypsin systems. The results on fluorination effect on coiled-coil dimerization and substrate affinities in the chymotrypsin active site obtained by MD, molecular docking and free energy calculations are in strong agreement with experimental data obtained by our collaborators. In particular, we have shown that fluorine content and position of fluorination can considerably change the polarity and steric properties of an amino acid side chain and, thus, can influence the properties that a fluorinated amino acid reveals within a native protein environment. III. Coiled-coils typically consist of two to five right-handed α-helices that wrap around each other to form a left-handed superhelix. The interface of two α-helices is usually represented by hydrophobic residues. However, the analysis of protein databases revealed that in natural occurring proteins up to 20% of these positions are populated by polar and charged residues. The impact of these residues on stability of coiled-coil system is not clear. MD simulations together with free energy calculations have been utilized to estimate favourable interaction partners for uncommon amino acids within the hydrophobic core of coiled-coils (Chapter 4). Based on these data, the best hits among binding partners for one strand of a coiled-coil bearing a charged amino acid in a central hydrophobic core position have been selected. Computational data have been in agreement with the results obtained by our collaborators, who applied phage display technology and CD spectroscopy. This combination of theoretical and experimental approaches allowed to get a deeper insight into the stability of the coiled-coil system. To conclude, this thesis widens existing concepts of protein structural biology in three areas of its current importance. We expand on the role of solvent in protein interfaces, which contributes to the knowledge of physico-chemical properties underlying protein-protein interactions. We develop a deeper insight into the understanding of the fluorine's impact upon its introduction into protein environments, which may assist in exploiting the full potential of fluorine's unique properties for applications in the field of protein engineering and drug design. Finally we investigate the mechanisms underlying coiled-coil system folding. The results presented in the thesis are of definite importance for possible applications (e.g. introduction of solvent explicitly into the scoring function) into protein folding, docking and rational design methods. The dissertation consists of four chapters: ● Chapter 1 contains an introduction to the topic of protein-protein interactions including basic concepts and an overview of the present state of research in the field. ● Chapter 2 focuses on the studies of the role of solvent in protein interfaces. ● Chapter 3 is devoted to the work on fluorinated amino acids in protein environments. ● Chapter 4 describes the study of coiled-coils folding properties. The experimental parts presented in Chapters 3 and 4 of this thesis have been performed by our collaborators at FU Berlin. Sections 2.1, 2.2, 3.1, 3.2 and Chapter 4 have been submitted/published in peer-reviewed international journals. Their organization follows a standard research article structure: Abstract, Introduction, Methodology, Results and discussion, and Conclusions. Section 3.3, though not published yet, is also organized in the same way. The literature references are summed up together at the end of the thesis to avoid redundancy within different chapters

High-Throughput Atomistic Modeling of Biomolecular Structure and Association

The reliability of many protein models arising from structure prediction methods is unclear. Here we present a method for absolute quality control of theoretical protein models, which can significantly contribute to their acceptance in the life-science research. We apply these methods to gain insight into the family of hydrophobins and modify them for increased cell adhesion to allow for the coating of implants. The novel proteins were shown to bind cells, while impeding bacterial adhesion

Strengths and Weaknesses of Hybrid TPR Technology for Obtaining Structural and Mechanistic Insights into TPR Proteins

Tetratricopeptide (TPR) repeats are a 34-residue helix-turn-helix motif that when repeated pack into a superhelical structure. TPR domains are frequently found mediating protein-protein interactions, often through a central groove. One protein complex bearing numerous TPR repeats is the Anaphase Promoting Complex (APC). The anaphase-promoting complex (APC) is a multi-subunit complex, which orchestrates mitotic cell cycles. APC is an E3 ligase in the ubiquitin cascade, and directs the 26S proteosome degradation of cell-cycle regulators. Throughout mitotic progression, proteins that are key regulators of the cell cycle are assembled with polyubiquitin chains by APC. One domain of the human APC is comprised of four related TPR proteins, APC8, APC6, APC3, and APC7, with each found in pairs. Crystal structures of some of these indicate that each has an N-terminal dimerization domain and a C-terminal domain that APC3 extends away from the dimer interface. The TPR C-terminal domains are thought to play major roles in mediating protein interactions within the APC. The subunit APC3 plays major roles in regulating APC function. Within an APC3 dimer, each C-terminal domain recruits the Ile-Arg motifs of substrate coreceptors Cdh1 (or Cdc20) and APC10. Cdh1 and APC10 together recruit substrates for ubiquitination. Therefore, it is important to understand the structure of APC3, and how APC3 mediates interactions. To address this problem, I used a novel “hybrid TPR” technology, in which some TPRs from a distant relative of APC3 are fused upstream of the C-terminal domain from human APC3. This approach enabled determination of a 3Å resolution structure encompassing the sequence of the APC3 C-terminal domain. Interestingly, only a fraction of the structure resembles canonical TPR repeats. Interpretation of the crystal structure based on published structures of complexes between TPR proteins and their partners, and on published electron microscopy structures of APC-Cdh1-APC10, reveal that the region containing the Cdh1/APC10 binding site adopts 3 canonical TPR repeats. The remainder of the portion of the structure corresponding to human APC3 is folded into an alternative conformation, in which a helix from the atypical portion of APC3 buries the Cdh1/APC10-interacting groove within the crystal. Accordingly, unlike wildtype APC3, the hybrid TPR APC3 fails to bind Cdh1 and APC10. Nonetheless, the crystal structure of “hybrid TPR APC3 C-terminal domain” allows the prediction of potentially important residues for binding to Cdh1 and APC10. Taken together, the data reveal strengths and weaknesses of hybrid TPR technology for obtaining structural insights into TPR subunits of multiprotein assemblies such as APC