Computational prediction of native protein ligand-binding and enzyme active site sequences

Investigation and drug design for novel molecules from natural products as inhibitors for controlling multiple myeloma disease using in-silico tools

Multiple myeloma (MM) is a disease that causes plasma cell growth in the bone marrow and immune globulin buildup in blood and urine. Despite recent advances in MM therapy, many still die due to its high mortality rate. A study using computational simulations analyzed 100 natural ingredients from the SANC database to determine if they inhibited the IgH domain, a known cause of multiple myeloma. Natural component Diospyrin inhibited the IgH enzyme with the best binding energy of -10.3 kcal/mol and three carbon-hydrogen bonds, followed by Parviflorone F complex with a binding energy of -10.1 kcal/mol and two conventional-hydrogen bonds. As a result, the Molecular Dynamic simulation was used to test the stability of the two complexes. During the simulation, the Diospyrin molecule dissociated from the protein at roughly 67.5 ns, whereas the Parviflorone F molecule stayed attached to the protein throughout. The latter was the subject of the investigation. The analysis of the production run data revealed that the Parviflorone F molecule exhibits a variety of conformations within the binding pocket while keeping a relatively constant distance from the protein's center of mass. The analysis of the production run data revealed that the Parviflorone F molecule exhibited a variety of conformations within the binding pocket while keeping a relatively constant distance from the protein's center of mass. The root mean square deviation (RMSD) plots for both the protein and complex showed a stable and steady average value of 4.4 Å for the first 82 nanoseconds of manufacture. As a result, the average value increased to 8.3 Å. Furthermore, the components of the binding free energy, as computed by MM-GBSA, revealed that the mean binding energy of the Parviflorone F molecule was -23.88 kcal/mol. Finally, after analyzing all of the examination data, Parviflorone F was identified as a powerful inhibitor of the IgH domain and hence of the MM disease, which requires further in-vivo conformation.Communicated by Ramaswamy H. Sarma.

Evolution of Organic Solvent-Resistant DNA Polymerases

The introduction of thermostable polymerases revolutionized the polymerase chain reaction (PCR) and biotechnology. However, many GC-rich genes cannot be PCR-amplified with high efficiency in water, irrespective of temperature. Although polar organic cosolvents can enhance nucleic acid polymerization and amplification by destabilizing duplex DNA and secondary structures, nature has not selected for the evolution of solvent-tolerant polymerase enzymes. Here, we used ultrahigh-throughput droplet-based selection and deep sequencing along with computational free-energy and binding affinity calculations to evolve Taq polymerase to generate enzymes that are both stable and highly active in the presence of organic cosolvents, resulting in up to 10% solvent resistance and over 100-fold increase in stability at 97.5 °C in the presence of 1,4-butanediol, as well as tolerance to up to 10 times higher concentrations of the potent cosolvents sulfolane and 2-pyrrolidone. Using these polymerases, we successfully amplified a broad spectrum of GC-rich templates containing regions with over 90% GC content, including templates recalcitrant to amplification with existing polymerases, even in the presence of cosolvents. We also demonstrated dramatically reduced GC bias in the amplification of genes with widely varying GC content in quantitative polymerase chain reaction (qPCR). By expanding the scope of solvent systems compatible with nucleic acid polymerization, these organic solvent-resistant polymerases enable a dramatic reduction of sequence bias not achievable through thermal resistance alone, with significant implications for a wide range of applications including sequencing and synthetic biology in mixed aqueous-organic media.

Modeling of SARS-CoV-2 Virus Proteins: Implications on Its Proteome

COronaVIrus Disease 19 (COVID-19) is a severe acute respiratory syndrome (SARS) caused by a group of beta coronaviruses, SARS-CoV-2. The SARS-CoV-2 virus is similar to previous SARS- and MERS-causing strains and has infected nearly six hundred and fifty million people all over the globe, while the death toll has crossed the six million mark (as of December, 2022). In this chapter, we look at how computational modeling approaches of the viral proteins could help us understand the various processes in the viral life cycle inside the host, an understanding of which might provide key insights in mitigating this and future threats. This understanding helps us identify key targets for the purpose of drug discovery and vaccine development.

AI protein structure prediction-based modeling and mutagenesis of a protostome receptor and peptide ligands reveal key residues for their interaction

The protostome leucokinin (LK) signaling system, including LK peptides and their G protein-coupled receptors, has been characterized in several species. Despite progress in this area, molecular mechanisms governing LK peptide-receptor interactions remain to be elucidated. Previously, we identified a precursor protein for Aplysia leucokinin-like peptides (ALKs) that contains the greatest number of amidated peptides among LK precursors in all species identified so far. Here, we identified the first ALK receptor from Aplysia, ALKR. We used cell-based IP1 activation assays to demonstrate that the two ALK peptides with the most copies, ALK1 and ALK2, activated ALKR with high potencies. Other endogenous ALK-derived peptides bearing the FXXWX-amide motif also activated ALKR to various degrees. Our examination of cross-species activity of ALKs with the Anopheles LKR was consistent with a critical role for the FXXWX-amide motif in receptor activity. Furthermore, we showed, through alanine substitution of ALK1, the highly conserved phenylalanine (F), tryptophan (W), and C-terminal amidation were each essential for receptor activation. Finally, we used an AI-based protein structure prediction server (Robetta) and Autodock Vina to predict the ligand-bound conformation of ALKR. Our model predicted several interactions (i.e., hydrophobic interactions, hydrogen bonds, and amide-pi stacking) between ALK peptides and ALKR, and several of our substitution and mutagenesis experiments were consistent with the predicted model. In conclusion, our results provide important information defining the possible interactions between ALK peptides and their receptors. The workflow utilized here may be useful for studying other ligand-receptor interactions for a neuropeptide signaling system, particularly in protostomes.

Conformational investigation of the structure-activity relationship of GdFFD and its analogues on an achatin-like neuropeptide receptor of Aplysia californica involved in the feeding circuit

Proteins and peptides in nature are almost exclusively made from l-amino acids, and this is even more absolute in the metazoan. With the advent of modern bioanalytical techniques, however, previously unappreciated roles for d-amino acids in biological processes have been revealed. Over 30 d-amino acid containing peptides (DAACPs) have been discovered in animals where at least one l-residue has been isomerized to the d-form via an enzyme-catalyzed process. In Aplysia californica, GdFFD and GdYFD (the lower-case letter "d" indicates a d-amino acid residue) modulate the feeding behavior by activating the Aplysia achatin-like neuropeptide receptor (apALNR). However, little is known about how the three-dimensional conformation of DAACPs influences activity at the receptor, and the role that d-residues play in these peptide conformations. Here, we use a combination of computational modeling, drift-tube ion-mobility mass spectrometry, and receptor activation assays to create a simple model that predicts bioactivities for a series of GdFFD analogs. Our results suggest that the active conformations of GdFFD and GdYFD are similar to their lowest energy conformations in solution. Our model helps connect the predicted structures of GdFFD analogs to their activities, and highlights a steric effect on peptide activity at position 1 on the GdFFD receptor apALNR. Overall, these methods allow us to understand ligand-receptor interactions in the absence of high-resolution structural data.

Protein:Ligand binding free energies: A stringent test for computational protein design

A computational protein design method is extended to allow Monte Carlo simulations where two ligands are titrated into a protein binding pocket, yielding binding free energy differences. These provide a stringent test of the physical model, including the energy surface and sidechain rotamer definition. As a test, we consider tyrosyl-tRNA synthetase (TyrRS), which has been extensively redesigned experimentally. We consider its specificity for its substrate l-tyrosine (l-Tyr), compared to the analogs d-Tyr, p-acetyl-, and p-azido-phenylalanine (ac-Phe, az-Phe). We simulate l- and d-Tyr binding to TyrRS and six mutants, and compare the structures and binding free energies to a more rigorous "MD/GBSA" procedure: molecular dynamics with explicit solvent for structures and a Generalized Born + Surface Area model for binding free energies. Next, we consider l-Tyr, ac- and az-Phe binding to six other TyrRS variants. The titration results are sensitive to the precise rotamer definition, which involves a short energy minimization for each sidechain pair to help relax bad contacts induced by the discrete rotamer set. However, when designed mutant structures are rescored with a standard GBSA energy model, results agree well with the more rigorous MD/GBSA. As a third test, we redesign three amino acid positions in the substrate coordination sphere, with either l-Tyr or d-Tyr as the ligand. For two, we obtain good agreement with experiment, recovering the wildtype residue when l-Tyr is the ligand and a d-Tyr specific mutant when d-Tyr is the ligand. For the third, we recover His with either ligand, instead of wildtype Gln.

Coupling Protein Side-Chain and Backbone Flexibility Improves the Re-design of Protein-Ligand Specificity

Interactions between small molecules and proteins play critical roles in regulating and facilitating diverse biological functions, yet our ability to accurately re-engineer the specificity of these interactions using computational approaches has been limited. One main difficulty, in addition to inaccuracies in energy functions, is the exquisite sensitivity of protein–ligand interactions to subtle conformational changes, coupled with the computational problem of sampling the large conformational search space of degrees of freedom of ligands, amino acid side chains, and the protein backbone. Here, we describe two benchmarks for evaluating the accuracy of computational approaches for re-engineering protein-ligand interactions: (i) prediction of enzyme specificity altering mutations and (ii) prediction of sequence tolerance in ligand binding sites. After finding that current state-of-the-art “fixed backbone” design methods perform poorly on these tests, we develop a new “coupled moves” design method in the program Rosetta that couples changes to protein sequence with alterations in both protein side-chain and protein backbone conformations, and allows for changes in ligand rigid-body and torsion degrees of freedom. We show significantly increased accuracy in both predicting ligand specificity altering mutations and binding site sequences. These methodological improvements should be useful for many applications of protein – ligand design. The approach also provides insights into the role of subtle conformational adjustments that enable functional changes not only in engineering applications but also in natural protein evolution.

Designing new protein–ligand interactions has tremendous potential for engineering sensitive biosensors for diagnostics or new enzymes useful in biotechnology, but these applications are extremely challenging, both because of inaccuracies of the energy functions used in modeling and design, and because protein active and binding sites are highly sensitive to subtle changes in structure. Here we describe a new method that addresses the second problem and couples changes in the structure of the protein backbone and of the amino acid side chains, the amino acid sequence, and the conformation of the ligand and its orientation in the binding site. We show that our method improvements significantly increase the accuracy of designing protein–ligand interactions compared to current state-of-the-art design methods. We assess these improvements in two important tests: the first predicts mutations that change ligand-binding preferences in enzymes, and the second predicts protein sequences that bind a given ligand. In these tests, subtle conformational changes made in our model are essential to recapitulate both the results from engineering experiments and the sequence diversity occurring in natural protein families. These results therefore shed light on the mechanisms of how new protein functions might have emerged and can be engineered in the laboratory.

Computational design of enzyme-ligand binding using a combined energy function and deterministic sequence optimization algorithm

Enzyme amino-acid sequences at ligand-binding interfaces are evolutionarily optimized for reactions, and the natural conformation of an enzyme-ligand complex must have a low free energy relative to alternative conformations in native-like or non-native sequences. Based on this assumption, a combined energy function was developed for enzyme design and then evaluated by recapitulating native enzyme sequences at ligand-binding interfaces for 10 enzyme-ligand complexes. In this energy function, the electrostatic interaction between polar or charged atoms at buried interfaces is described by an explicitly orientation-dependent hydrogen-bonding potential and a pairwise-decomposable generalized Born model based on the general side chain in the protein design framework. The energy function is augmented with a pairwise surface-area based hydrophobic contribution for nonpolar atom burial. Using this function, on average, 78% of the amino acids at ligand-binding sites were predicted correctly in the minimum-energy sequences, whereas 84% were predicted correctly in the most-similar sequences, which were selected from the top 20 sequences for each enzyme-ligand complex. Hydrogen bonds at the enzyme-ligand binding interfaces in the 10 complexes were usually recovered with the correct geometries. The binding energies calculated using the combined energy function helped to discriminate the active sequences from a pool of alternative sequences that were generated by repeatedly solving a series of mixed-integer linear programming problems for sequence selection with increasing integer cuts.

Dynamics and control of DNA sequence amplification

DNA amplification is the process of replication of a specified DNA sequence in vitro through time-dependent manipulation of its external environment. A theoretical framework for determination of the optimal dynamic operating conditions of DNA amplification reactions, for any specified amplification objective, is presented based on first-principles biophysical modeling and control theory. Amplification of DNA is formulated as a problem in control theory with optimal solutions that can differ considerably from strategies typically used in practice. Using the Polymerase Chain Reaction as an example, sequence-dependent biophysical models for DNA amplification are cast as control systems, wherein the dynamics of the reaction are controlled by a manipulated input variable. Using these control systems, we demonstrate that there exists an optimal temperature cycling strategy for geometric amplification of any DNA sequence and formulate optimal control problems that can be used to derive the optimal temperature profile. Strategies for the optimal synthesis of the DNA amplification control trajectory are proposed. Analogous methods can be used to formulate control problems for more advanced amplification objectives corresponding to the design of new types of DNA amplification reactions.

Mechanism of inhibition of the human sirtuin enzyme SIRT3 by nicotinamide: computational and experimental studies

Sirtuins are key regulators of many cellular functions including cell growth, apoptosis, metabolism, and genetic control of age-related diseases. Sirtuins are themselves regulated by their cofactor nicotinamide adenine dinucleotide (NAD+) as well as their reaction product nicotinamide (NAM), the physiological concentrations of which vary during the process of aging. Nicotinamide inhibits sirtuins through the so-called base exchange pathway, wherein rebinding of the reaction product to the enzyme accelerates the reverse reaction. We investigated the mechanism of nicotinamide inhibition of human SIRT3, the major mitochondrial sirtuin deacetylase, in vitro and in silico using experimental kinetic analysis and Molecular Mechanics-Poisson Boltzmann/Generalized Born Surface Area (MM-PB(GB)SA) binding affinity calculations with molecular dynamics sampling. Through experimental kinetic studies, we demonstrate that NAM inhibition of SIRT3 involves apparent competition between the inhibitor and the enzyme cofactor NAD+, contrary to the traditional characterization of base exchange as noncompetitive inhibition. We report a model for base exchange inhibition that relates such kinetic properties to physicochemical properties, including the free energies of enzyme-ligand binding, and estimate the latter through the first reported computational binding affinity calculations for SIRT3:NAD+, SIRT3:NAM, and analogous complexes for Sir2. The computational results support our kinetic model, establishing foundations for quantitative modeling of NAD+/NAM regulation of mammalian sirtuins during aging and the computational design of sirtuin activators that operate through alleviation of base exchange inhibition.

Systematic optimization model and algorithm for binding sequence selection in computational enzyme design

A systematic optimization model for binding sequence selection in computational enzyme design was developed based on the transition state theory of enzyme catalysis and graph-theoretical modeling. The saddle point on the free energy surface of the reaction system was represented by catalytic geometrical constraints, and the binding energy between the active site and transition state was minimized to reduce the activation energy barrier. The resulting hyperscale combinatorial optimization problem was tackled using a novel heuristic global optimization algorithm, which was inspired and tested by the protein core sequence selection problem. The sequence recapitulation tests on native active sites for two enzyme catalyzed hydrolytic reactions were applied to evaluate the predictive power of the design methodology. The results of the calculation show that most of the native binding sites can be successfully identified if the catalytic geometrical constraints and the structural motifs of the substrate are taken into account. Reliably predicting active site sequences may have significant implications for the creation of novel enzymes that are capable of catalyzing targeted chemical reactions.

Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study

BACKGROUND

There is a need for automated methods to learn general features of the interactions of a ligand class with its diverse set of protein receptors. An appropriate machine learning approach is Inductive Logic Programming (ILP), which automatically generates comprehensible rules in addition to prediction. The development of ILP systems which can learn rules of the complexity required for studies on protein structure remains a challenge. In this work we use a new ILP system, ProGolem, and demonstrate its performance on learning features of hexose-protein interactions.

RESULTS

The rules induced by ProGolem detect interactions mediated by aromatics and by planar-polar residues, in addition to less common features such as the aromatic sandwich. The rules also reveal a previously unreported dependency for residues cys and leu. They also specify interactions involving aromatic and hydrogen bonding residues. This paper shows that Inductive Logic Programming implemented in ProGolem can derive rules giving structural features of protein/ligand interactions. Several of these rules are consistent with descriptions in the literature.

CONCLUSIONS

In addition to confirming literature results, ProGolem's model has a 10-fold cross-validated predictive accuracy that is superior, at the 95% confidence level, to another ILP system previously used to study protein/hexose interactions and is comparable with state-of-the-art statistical learners.

Computational design of protein-ligand binding: modifying the specificity of asparaginyl-tRNA synthetase

A method for computational design of protein-ligand interactions is implemented and tested on the asparaginyl- and aspartyl-tRNA synthetase enzymes (AsnRS, AspRS). The substrate specificity of these enzymes is crucial for the accurate translation of the genetic code. The method relies on a molecular mechanics energy function and a simple, continuum electrostatic, implicit solvent model. As test calculations, we first compute AspRS-substrate binding free energy changes due to nine point mutations, for which experimental data are available; we also perform large-scale redesign of the entire active site of each enzyme (40 amino acids) and compare to experimental sequences. We then apply the method to engineer an increased binding of aspartyl-adenylate (AspAMP) into AsnRS. Mutants are obtained using several directed evolution protocols, where four or five amino acid positions in the active site are randomized. Promising mutants are subjected to molecular dynamics simulations; Poisson-Boltzmann calculations provide an estimate of the corresponding, AspAMP, binding free energy changes, relative to the native AsnRS. Several of the mutants are predicted to have an inverted binding specificity, preferring to bind AspAMP rather than the natural substrate, AsnAMP. The computed binding affinities are significantly weaker than the native, AsnRS:AsnAMP affinity, and in most cases, the active site structure is significantly changed, compared to the native complex. This almost certainly precludes catalytic activity. One of the designed sequences has a higher affinity and more native-like structure and may represent a valid candidate for Asp activity.

Molecular surface mesh generation by filtering electron density map

Bioinformatics applied to macromolecules are now widely spread and in continuous expansion. In this context, representing external molecular surface such as the Van der Waals Surface or the Solvent Excluded Surface can be useful for several applications. We propose a fast and parameterizable algorithm giving good visual quality meshes representing molecular surfaces. It is obtained by isosurfacing a filtered electron density map. The density map is the result of the maximum of Gaussian functions placed around atom centers. This map is filtered by an ideal low-pass filter applied on the Fourier Transform of the density map. Applying the marching cubes algorithm on the inverse transform provides a mesh representation of the molecular surface.

Isolation, characterization and structural studies of amorpha - 4, 11-diene synthase (ADS(3963)) from Artemisia annua L

With the escalating prevalence of malaria in recent years, artemisinin demand has placed considerable stress on its production worldwide. At present, the relative low-yield of artemisinin (0.01-1.1 %) in the source plant (Artemisia annua L. plant) has imposed a serious limitation in commercializing the drug. Amorpha-4, 11-diene synthase (ADS) has been reported a key enzyme in enhancing the artemisinin level in Artemisia annua L. An understanding of the structural and functional correlations of Amorpha-4, 11-diene synthase (ADS) may therefore, help in the molecular up-regulation of the enzyme. In this context, an in silico approach was used to study the ADS₃₉₆₃ (3963 bp) gene cloned by us, from high artemisinin (0.7-0.9% dry wt basis) yielding strain of A. annua L. The full-length putative gene of ADS₃₉₆₃ was found to encode a protein consisting of 533 amino acid residues with conserved aspartate rich domain. The isoelectric point (pI) and molecular weight of the protein were 5.25 and 62.2 kDa, respectively. The phylogenetic analysis of ADS genes from various species revealed evolutionary conservation. Homology modeling method was used for prediction of the 3D structure of ADS₃₉₆₃ protein and Autodock 4.0 version was used to study the ligand binding. The predicted 3D model and docking studies may further be used in characterizing the protein in wet laboratory.

An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge

Hexoses are simple sugars that play a key role in many cellular pathways, and in the regulation of development and disease mechanisms. Current protein-sugar computational models are based, at least partially, on prior biochemical findings and knowledge. They incorporate different parts of these findings in predictive black-box models. We investigate the empirical support for biochemical findings by comparing Inductive Logic Programming (ILP) induced rules to actual biochemical results. We mine the Protein Data Bank for a representative data set of hexose binding sites, non-hexose binding sites and surface grooves. We build an ILP model of hexose-binding sites and evaluate our results against several baseline machine learning classifiers. Our method achieves an accuracy similar to that of other black-box classifiers while providing insight into the discriminating process. In addition, it confirms wet-lab findings and reveals a previously unreported Trp-Glu amino acids dependency.

Challenges in the computational design of proteins

Protein design has many applications not only in biotechnology but also in basic science. It uses our current knowledge in structural biology to predict, by computer simulations, an amino acid sequence that would produce a protein with targeted properties. As in other examples of synthetic biology, this approach allows the testing of many hypotheses in biology. The recent development of automated computational methods to design proteins has enabled proteins to be designed that are very different from any known ones. Moreover, some of those methods mostly rely on a physical description of atomic interactions, which allows the designed sequences not to be biased towards known proteins. In this paper, we will describe the use of energy functions in computational protein design, the use of atomic models to evaluate the free energy in the unfolded and folded states, the exploration and optimization of amino acid sequences, the problem of negative design and the design of biomolecular function. We will also consider its use together with the experimental techniques such as directed evolution. We will end by discussing the challenges ahead in computational protein design and some of their future applications.

Improving the species cross-reactivity of an antibody using computational design

The high degree of specificity displayed by antibodies often results in varying potencies against antigen orthologs, which can affect the efficacy of these molecules in different animal models of disease. We have used a computational design strategy to improve the species cross-reactivity of an antibody-based inhibitor of the cancer-associated serine protease MT-SP1. In silico predictions were tested in vitro, and the most effective mutation, T98R, was shown to improve antibody affinity for the mouse ortholog of the enzyme 14-fold, resulting in an inhibitor with a K(I) of 340 pM. This improved affinity will be valuable when exploring the role of MT-SP1 in mouse models of cancer, and the strategy outlined here could be useful in fine-tuning antibody specificity.

TransCent: computational enzyme design by transferring active sites and considering constraints relevant for catalysis

Background

Computational enzyme design is far from being applicable for the general case. Due to computational complexity and limited knowledge of the structure-function interplay, heuristic methods have to be used.

Results

We have developed TransCent, a computational enzyme design method supporting the transfer of active sites from one enzyme to an alternative scaffold. In an optimization process, it balances requirements originating from four constraints. These are 1) protein stability, 2) ligand binding, 3) pK_a values of active site residues, and 4) structural features of the active site. Each constraint is handled by an individual software module. Modules processing the first three constraints are based on state-of-the-art concepts, i.e. RosettaDesign, DrugScore, and PROPKA. To account for the fourth constraint, knowledge-based potentials are utilized. The contribution of modules to the performance of TransCent was evaluated by means of a recapitulation test. The redesign of oxidoreductase cytochrome P450 was analyzed in detail. As a first application, we present and discuss models for the transfer of active sites in enzymes sharing the frequently encountered triosephosphate isomerase fold.

Conclusion

A recapitulation test on native enzymes showed that TransCent proposes active sites that resemble the native enzyme more than those generated by RosettaDesign alone. Additional tests demonstrated that each module contributes to the overall performance in a statistically significant manner.

Exceptional amyloid beta peptide hydrolyzing activity of nonphysiological immunoglobulin variable domain scaffolds

Nucleophilic sites in the paired variable domains of the light and heavy chains (VL and VH domains) of Ig can catalyze peptide bond hydrolysis. Amyloid beta (Abeta)-binding Igs are under consideration for immunotherapy of Alzheimer disease. We searched for Abeta-hydrolyzing human IgV domains (IgVs) in a library containing a majority of single chain Fv clones mimicking physiological VL-VH-combining sites and minority IgV populations with nonphysiological structures generated by cloning errors. Random screening and covalent selection of phage-displayed IgVs with an electrophilic Abeta analog identified rare IgVs that hydrolyzed Abeta mainly at His14-Gln15. Inhibition of IgV catalysis and irreversible binding by an electrophilic hapten suggested a nucleophilic catalytic mechanism. Structural analysis indicated that the catalytic IgVs are nonphysiological structures, a two domain heterodimeric VL (IgVL2-t) and single domain VL clones with aberrant polypeptide tags (IgVL-t'). The IgVs hydrolyzed Abeta at rates superior to naturally occurring Igs by 3-4 orders of magnitude. Forced pairing of the single domain VL with VH or VL domains resulted in reduced Abeta hydrolysis, suggesting catalysis by the unpaired VL domain.Angstrom level amino acid displacements evident in molecular models of the two domain and unpaired VL domain clones explain alterations of catalytic activity. In view of their superior catalytic activity, the VL domain IgVs may help attain clearance of medically important antigens more efficiently than natural Igs.

An insight into the general relationship between the three dimensional structures of enzymes and their electronic wave functions: Implication for the prediction of functional sites of enzymes

In this study, we explored the general relationship between the three-dimensional (3D) structures of enzymes and their electronic wave functions. Furthermore, we developed a method for the prediction of their functionally important sites. For this purpose, we first performed linear-scaling molecular orbital calculations for 112 nonredundant, non-homologous enzymes with known structure and function. In consequence, we showed that the canonical molecular orbitals (MOs) of the enzymes could be classified into three groups according to the degree of electron delocalization: highly localized orbitals (Group A), highly delocalized orbitals whose electrons are distributed over almost the whole molecule (Group B), and moderately delocalized orbitals (Group C). The MOs belonging to Group A are located near the HOMO-LUMO band gap, and thereby include the frontier orbitals of a given enzyme. We inferred that the MOs of Group B play a role in stabilizing the 3D structure of the enzyme, while those of Group C contribute to constructing the covalent bond framework of the enzyme. Next, we investigated whether the frontier orbitals of enzymes could be used for identifying their potential functional sites. As a result, we found that the frontier orbitals of the 112 enzymes have a high propensity to be colocalized with the known functional sites, especially when the enzymes are hydrated. Such a propensity is shown to be remarkable when Glu or Asp is a functional site residue. On the basis of these results, we finally propose a protocol for the prediction of functional sites of enzymes.

A computational framework to empower probabilistic protein design

MOTIVATION

The task of engineering a protein to perform a target biological function is known as protein design. A commonly used paradigm casts this functional design problem as a structural one, assuming a fixed backbone. In probabilistic protein design, positional amino acid probabilities are used to create a random library of sequences to be simultaneously screened for biological activity. Clearly, certain choices of probability distributions will be more successful in yielding functional sequences. However, since the number of sequences is exponential in protein length, computational optimization of the distribution is difficult.

RESULTS

In this paper, we develop a computational framework for probabilistic protein design following the structural paradigm. We formulate the distribution of sequences for a structure using the Boltzmann distribution over their free energies. The corresponding probabilistic graphical model is constructed, and we apply belief propagation (BP) to calculate marginal amino acid probabilities. We test this method on a large structural dataset and demonstrate the superiority of BP over previous methods. Nevertheless, since the results obtained by BP are far from optimal, we thoroughly assess the paradigm using high-quality experimental data. We demonstrate that, for small scale sub-problems, BP attains identical results to those produced by exact inference on the paradigmatic model. However, quantitative analysis shows that the distributions predicted significantly differ from the experimental data. These findings, along with the excellent performance we observed using BP on the smaller problems, suggest potential shortcomings of the paradigm. We conclude with a discussion of how it may be improved in the future.

Design of protein-ligand binding based on the molecular-mechanics energy model

While the molecular-mechanics field has standardized on a few potential energy functions, computational protein design efforts are based on potentials that are unique to individual laboratories. Here we show that a standard molecular-mechanics potential energy function without any modifications can be used to engineer protein-ligand binding. A molecular-mechanics potential is used to reconstruct the coordinates of various binding sites with an average root-mean-square error of 0.61 A and to reproduce known ligand-induced side-chain conformational shifts. Within a series of 34 mutants, the calculation can always distinguish between weak (K(d)>1 mM) and tight (K(d)<10 microM) binding sequences. Starting from partial coordinates of the ribose-binding protein lacking the ligand and the 10 primary contact residues, the molecular-mechanics potential is used to redesign a ribose-binding site. Out of a search space of 2 x 10(12) sequences, the calculation selects a point mutant of the native protein as the top solution (experimental K(d)=17 microM) and the native protein as the second best solution (experimental K(d)=210 nM). The quality of the predictions depends on the accuracy of the generalized Born electrostatics model, treatment of protonation equilibria, high-resolution rotamer sampling, a final local energy minimization step, and explicit modeling of the bound, unbound, and unfolded states. The application of unmodified molecular-mechanics potentials to protein design links two fields in a mutually beneficial way. Design provides a new avenue for testing molecular-mechanics energy functions, and future improvements in these energy functions will presumably lead to more accurate design results.

Progress in computational protein design

Current progress in computational structure-based protein design is reviewed in the areas of methodology and applications. Foundational advances include new potential functions, more efficient ways of computing energetics, flexible treatments of solvent, and useful energy function approximations, as well as ensemble-based approaches to scoring designs for inclusion of entropic effects, improvements to guaranteed and to stochastic search techniques, and methods to design combinatorial libraries for screening and selection. Applications include new approaches and successes in the design of specificity for protein folding, binding, and catalysis, in the redesign of proteins for enhanced binding affinity, and in the application of design technology to study and alter enzyme catalysis. Computational protein design continues to mature and advance.

Combinatorial methods for small-molecule placement in computational enzyme design

The incorporation of small-molecule transition state structures into protein design calculations poses special challenges because of the need to represent the added translational, rotational, and conformational freedoms within an already difficult optimization problem. Successful approaches to computational enzyme design have focused on catalytic side-chain contacts to guide placement of small molecules in active sites. We describe a process for modeling small molecules in enzyme design calculations that extends previously described methods, allowing favorable small-molecule positions and conformations to be explored simultaneously with sequence optimization. Because all current computational enzyme design methods rely heavily on sampling of possible active site geometries from discrete conformational states, we tested the effects of discretization parameters on calculation results. Rotational and translational step sizes as well as side-chain library types were varied in a series of computational tests designed to identify native-like binding contacts in three natural systems. We find that conformational parameters, especially the type of rotamer library used, significantly affect the ability of design calculations to recover native binding-site geometries. We describe the construction and use of a crystallographic conformer library and find that it more reliably captures active-site geometries than traditional rotamer libraries in the systems tested.

Point mutations in protein globular domains: contributions from function, stability and misfolding

Several contrasting hypotheses have been formulated about the influence of functional and conformational properties, like stability and avoidance of misfolding, on the evolution of protein globular domains. Selection at functional sites has been suggested to be detrimental to stability or coupled to it. Avoidance of misfolding may be achieved by discarding misfolding-prone sequences or by maintaining a stable native state and thus destabilizing partially or fully unfolded states from which misfolding can take place. We have performed a hierarchical analysis of a large database of point mutations to dissect the relative contributions of function, stability and misfolding in the evolution of natural sequences. We show that at catalytic sites, selection for function overrules selection for stability but find no evidence for an anticorrelation between function and stability. Selection for stability plays a secondary role at binding sites, but is not fully coupled to selection for function. Remarkably, we did not find a selective pressure against misfolding-prone sequences in globular proteins at the level of individual positions. We suggest that such a selection would compromise native-state stability due to a correlation between the stabilities of native and misfolded states. Stabilization of the native state is the most frequent way in which natural proteins avoid misfolding.

Sequence optimization and designability of enzyme active sites

We recently found that many residues in enzyme active sites can be computationally predicted by the optimization of scoring functions based on substrate binding affinity, subject to constraints on the geometry of catalytic residues and protein stability. Here, we explore the generality of this surprising observation. First, the impact of hydrogen-bonding networks necessary for catalysis on the accuracy of sequence optimization is assessed; incorporation of these networks, where relevant, into the set of catalytic constraints is found to be essential. Next, the impact of multiple substrate selectivity on sequence optimization is probed by carrying out independent calculations for complexes of deoxyribonucleoside kinases with various cognate ligands, revealing how simultaneous selection pressures determined active-site sequences of these enzymes. Including previous calculations on simpler enzymes, computational sequence optimization correctly predicts 76% of all active-site residues tested (86% correct, with 93% similar, for naturally conserved residues). In these studies, the ligand is fixed in its native conformation. To assess the applicability of these methods to de novo active-site design, the effect of small ligand motions around the native pose is also examined. Robustness of sequence accuracy for topologically similar poses is demonstrated for selected kinases, but not for a model peptidase. Based on these observations, we introduce the notion of the designability of an enzyme active site, a metric that may be used to guide the search for protein scaffolds suitable for the introduction of de novo activity for a desired chemical reaction.