Stability curve prediction of homologous proteins using temperature-dependent statistical potentials

High-throughput system for the thermostability analysis of proteins

Thermal stability of proteins is a primary metric for evaluating their physical properties. Although researchers attempted to predict it using machine learning frameworks, their performance has been dependent on the quality and quantity of published data. This is due to the technical limitation that thermodynamic characterization of protein denaturation by fluorescence or calorimetry in a high-throughput manner has been challenging. Obtaining a melting curve that derives solely from the target protein requires laborious purification, making it far from practical to prepare a hundred or more samples in a single workflow. Here, we aimed to overcome this throughput limitation by leveraging the high protein secretion efficacy of Brevibacillus and consecutive treatment with plate-scale purification methodologies. By handling the entire process of expression, purification, and analysis on a per-plate basis, we enabled the direct observation of protein denaturation in 384 samples within 4 days. To demonstrate a practical application of the system, we conducted a comprehensive analysis of 186 single mutants of a single-chain variable fragment of nivolumab, harvesting the melting temperature (Tm) ranging from -9.3 up to +10.8°C compared to the wild-type sequence. Our findings will allow for data-driven stabilization in protein design and streamlining the rational approaches.

Living in trinity of extremes: Genomic and proteomic signatures of halophilic, thermophilic, and pH adaptation

Since nucleic acids and proteins of unicellular prokaryotes are directly exposed to extreme environmental conditions, it is possible to explore the genomic-proteomic compositional determinants of molecular mechanisms of adaptation developed by them in response to harsh environmental conditions. Using a wealth of currently available complete genomes/proteomes we were able to explore signatures of adaptation to three environmental factors, pH, salinity, and temperature, observing major trends in compositions of their nucleic acids and proteins. We derived predictors of thermostability, halophilic, and pH adaptations and complemented them by the principal components analysis. We observed a clear difference between thermophilic and salinity/pH adaptations, whereas latter invoke seemingly overlapping mechanisms. The genome-proteome compositional trade-off reveals an intricate balance between the work of base paring and base stacking in stabilization of coding DNA and r/tRNAs, and, at the same time, universal requirements for the stability and foldability of proteins regardless of the nucleotide biases. Nevertheless, we still found hidden fingerprints of ancient evolutionary connections between the nucleotide and amino acid compositions indicating their emergence, mutual evolution, and adjustment. The evolutionary perspective on the adaptation mechanisms is further studied here by means of the comparative analysis of genomic/proteomic traits of archaeal and bacterial species. The overall picture of genomic/proteomic signals of adaptation obtained here provides a foundation for future engineering and design of functional biomolecules resistant to harsh environments.

Multi-epitope vaccine candidates based on mycobacterial membrane protein large (MmpL) proteins against Mycobacterium ulcerans

Buruli ulcer (BU) is a neglected tropical disease. It is caused by the bacterium Mycobacterium ulcerans and is characterized by skin lesions. Several studies were performed testing the Bacillus Calmette-Guérin (BCG) vaccine in human and animal models and M. ulcerans-specific vaccines in animal models. However, there are currently no clinically accepted vaccines to prevent M. ulcerans infection. The aim of this study was to identify T-cell and B-cell epitopes from the mycobacterial membrane protein large (MmpL) proteins of M. ulcerans. These epitopes were analysed for properties including antigenicity, immunogenicity, non-allergenicity, non-toxicity, population coverage and the potential to induce cytokines. The final 8 CD8+, 12 CD4+ T-cell and 5 B-cell epitopes were antigenic, non-allergenic and non-toxic. The estimated global population coverage of the CD8+ and CD4+ epitopes was 97.71%. These epitopes were used to construct five multi-epitope vaccine constructs with different adjuvants and linker combinations. The constructs underwent further structural analyses and refinement. The constructs were then docked with Toll-like receptors. Three of the successfully docked complexes were structurally analysed. Two of the docked complexes successfully underwent molecular dynamics simulations (MDS) and post-MDS analysis. The complexes generated were found to be stable. However, experimental validation of the complexes is required.

Identification of potential candidate vaccines against Mycobacterium ulcerans based on the major facilitator superfamily transporter protein

Buruli ulcer is a neglected tropical disease that is characterized by non-fatal lesion development. The causative agent is Mycobacterium ulcerans (M. ulcerans). There are no known vectors or transmission methods, preventing the development of control methods. There are effective diagnostic techniques and treatment routines; however, several socioeconomic factors may limit patients' abilities to receive these treatments. The Bacillus Calmette-Guérin vaccine developed against tuberculosis has shown limited efficacy, and no conventionally designed vaccines have passed clinical trials. This study aimed to generate a multi-epitope vaccine against M. ulcerans from the major facilitator superfamily transporter protein using an immunoinformatics approach. Twelve M. ulcerans genome assemblies were analyzed, resulting in the identification of 11 CD8+ and 7 CD4+ T-cell epitopes and 2 B-cell epitopes. These conserved epitopes were computationally predicted to be antigenic, immunogenic, non-allergenic, and non-toxic. The CD4+ T-cell epitopes were capable of inducing interferon-gamma and interleukin-4. They successfully bound to their respective human leukocyte antigens alleles in in silico docking studies. The expected global population coverage of the T-cell epitopes and their restricted human leukocyte antigens alleles was 99.90%. The population coverage of endemic regions ranged from 99.99% (Papua New Guinea) to 21.81% (Liberia). Two vaccine constructs were generated using the Toll-like receptors 2 and 4 agonists, LprG and RpfE, respectively. Both constructs were antigenic, non-allergenic, non-toxic, thermostable, basic, and hydrophilic. The DNA sequences of the vaccine constructs underwent optimization and were successfully in-silico cloned with the pET-28a(+) plasmid. The vaccine constructs were successfully docked to their respective toll-like receptors. Molecular dynamics simulations were carried out to analyze the binding interactions within the complex. The generated binding energies indicate the stability of both complexes. The constructs generated in this study display severable favorable properties, with construct one displaying a greater range of favorable properties. However, further analysis and laboratory validation are required.

In silico characterization of fructosyl peptide oxidase properties from Eupenicillium terrenum

Fructosyl peptide oxidase (FPOX) enzyme from Eupenicillium terrenum has a high potential to be applied as a diagnostic enzyme. The aim of the present study is the characterization of FPOX from E. terrenum using different bioinformatics tools. The computational prediction of the RNA and protein secondary structures of FPOX, solubility profile in Escherichia coli, stability, domains, and functional properties were performed. In the FPOX protein, six motifs were detected. The d-amino acid oxidase motif was found as the most important motif that is a FAD-dependent oxidoreductase. The cysteines including 97, 154, 234, 280, and 360 showed a lower score than -10 that have a low possibility for participitation in the formation of the SS bond. The 56.52% of FPOX amino acids are nonpolar. Random coils are dominant in the FPOX sequence, followed by alpha-helix and extended strand. The fpox gene is capable of generating a stable RNA secondary structure (-423.90 kcal/mol) in E. coli. FPOX has a large number of hydrophobic amino acids. FPOX showed a low solubility in E. coli which has several aggregation-prone sites in its 3-D structure. According to the scores, the best mutation candidate for increasing solubility was the conversion of methionine 302 to arginine. The melting temperature of FPOX based on its amino acid sequence was 55°C to 65°C. The amounts of thermodynamic parameters for the FPOX enzyme were -137.4 kcal/mol, -3.59 kcal/(mol K), and -6.8 kcal/mol for standard folding enthalpy, heat capacity, and folding free energy, respectively. In conclusion, the in silico study of proteins can provide a valuable method for better understanding the protein properties and functions for use in our purposes.

ProTstab2 for Prediction of Protein Thermal Stabilities

The stability of proteins is an essential property that has several biological implications. Knowledge about protein stability is important in many ways, ranging from protein purification and structure determination to stability in cells and biotechnological applications. Experimental determination of thermal stabilities has been tedious and available data have been limited. The introduction of limited proteolysis and mass spectrometry approaches has facilitated more extensive cellular protein stability data production. We collected melting temperature information for 34,913 proteins and developed a machine learning predictor, ProTstab2, by utilizing a gradient boosting algorithm after testing seven algorithms. The method performance was assessed on a blind test data set and showed a Pearson correlation coefficient of 0.753 and root mean square error of 7.005. Comparison to previous methods indicated that ProTstab2 had superior performance. The method is fast, so it was applied to predict and compare the stabilities of all proteins in human, mouse, and zebrafish proteomes for which experimental data were not determined. The tool is freely available.

Intrinsically disordered protein, DNA binding with one finger transcription factor (OsDOF27) implicates thermotolerance in yeast and rice

Intrinsically disorder regions or proteins (IDRs or IDPs) constitute a large subset of the eukaryotic proteome, which challenges the protein structure-function paradigm. These IDPs lack a stable tertiary structure, yet they play a crucial role in the diverse biological process of plants. This study represents the intrinsically disordered nature of a plant-specific DNA binding with one finger transcription factor (DOF-TF). Here, we have investigated the role of OsDOF27 and characterized it as an intrinsically disordered protein. Furthermore, the molecular role of OsDOF27 in thermal stress tolerance has been elucidated. The qRT-PCR analysis revealed that OsDOF27 was significantly upregulated under different abiotic stress treatments in rice, particularly under heat stress. The stress-responsive transcript induction of OsDOF27 was further correlated with enriched abiotic stress-related cis-regulatory elements present in its promoter region. The in vivo functional analysis of the potential role of OsDOF27 in thermotolerance was further studied in yeast and in planta. Ectopic expression of OsDOF27 in yeast implicates thermotolerance response. Furthermore, the rice transgenic lines with overexpressing OsDOF27 revealed a positive role in mitigating heat stress tolerance. Collectively, our results evidently show the intrinsically disorderedness in OsDOF27 and its role in thermal stress response in rice.

DDGun: an untrained predictor of protein stability changes upon amino acid variants

Estimating the functional effect of single amino acid variants in proteins is fundamental for predicting the change in the thermodynamic stability, measured as the difference in the Gibbs free energy of unfolding, between the wild-type and the variant protein (ΔΔG). Here, we present the web-server of the DDGun method, which was previously developed for the ΔΔG prediction upon amino acid variants. DDGun is an untrained method based on basic features derived from evolutionary information. It is antisymmetric, as it predicts opposite ΔΔG values for direct (A → B) and reverse (B → A) single and multiple site variants. DDGun is available in two versions, one based on only sequence information and the other one based on sequence and structure information. Despite being untrained, DDGun reaches prediction performances comparable to those of trained methods. Here we make DDGun available as a web server. For the web server version, we updated the protein sequence database used for the computation of the evolutionary features, and we compiled two new data sets of protein variants to do a blind test of its performances. On these blind data sets of single and multiple site variants, DDGun confirms its prediction performance, reaching an average correlation coefficient between experimental and predicted ΔΔG of 0.45 and 0.49 for the sequence-based and structure-based versions, respectively. Besides being used for the prediction of ΔΔG, we suggest that DDGun should be adopted as a benchmark method to assess the predictive capabilities of newly developed methods. Releasing DDGun as a web-server, stand-alone program and docker image will facilitate the necessary process of method comparison to improve ΔΔG prediction.

Exploring the Effect of Enhanced Sampling on Protein Stability Prediction

Changes in protein stability due to side-chain mutations are evaluated by alchemical free-energy calculations based on classical molecular dynamics (MD) simulations in explicit solvent using the GROMOS force field. Three proteins of different complexity with a total number of 93 single-point mutations are analyzed, and the relative free-energy differences are discussed with respect to configurational sampling and (dis)agreement with experimental data. For the smallest protein studied, a 34-residue WW domain, the starting structure dependence of the alchemical free-energy changes, is discussed in detail. Deviations from previous simulations for the two other proteins are shown to result from insufficient sampling in the earlier studies. Hamiltonian replica exchange in combination with multiple starting structures and sufficient sampling time of more than 100 ns per intermediate alchemical state is required in some cases to reach convergence.

Biodegradation of isoprene by Arthrobacter sp. strain BHU FT2: Genomics-proteomics enabled novel insights

The bacterial degradation of isoprene is important for maintaining its atmospheric concentration in unpolluted environment. It may be possible to use natural isoprene degrading bacteria in engineered systems to eliminate or limit isoprene emissions from various sources. Biodegradation of isoprene by Arthrobacter sp. strain BHU FT2 was investigated. The genome was found to contain 4151545 bp long chromosome having 3747 coding genes, and coded potential isoprene degrading enzymes. The molecular docking of monooxygenases with isoprene displayed a higher binding energy (-4.59 kcal/mol) for WP_015938387.1 monooxygenase. Analysis of the identified monooxygenases with the known isoprene monooxygenases revealed 67% sequence identity of WP_015938387.1 (Locus tag JHV56_10705) monooxygenase of the considered strain with the OPX16961.1 monooxygenase of Gordonia sp. i37 isoprene degrading starin. These results provided a strong evidence for the high isoprene degrading potential of the Arthrobacter sp. BHU FT2 which could be efficiently exploited for isoprene degradation in large scale bio-filtration units.

Characterization of a Stable Form of Carboxypeptidase G2 (Glucarpidase), a Potential Biobetter Variant, From Acinetobacter sp. 263903-1

Carboxypeptidase G2 (CPG2) is a bacterial enzyme widely used to detoxify methotrexate (MTX) and in enzyme/prodrug therapy for cancer treatment. However, several drawbacks, such as instability, have limited its efficiency. Herein, we have evaluated the properties of a putative CPG2 from Acinetobacter sp. 263903-1 (AcCPG2). AcCPG2 is compared with a CPG2 derived from Pseudomonas sp. strain RS-16 (PsCPG2), available as an FDA-approved medication called glucarpidase. After modeling AcCPG2 using the I-TASSER program, the refined model was validated by PROCHECK, VERIFY 3D and according to the Z score of the model. Using computational analyses, AcCPG2 displayed higher thermodynamic stability and a lower aggregation propensity than PsCPG2. AcCPG2 showed an optimum pH of 7.5 against MTX and was stable over a pH range of 5-10. AcCPG2 exhibited optimum activity at 50 °C and higher thermal stability at a temperature range of 20-70 °C compared to PsCPG2. The K_m value of the purified AcCPG2 toward folate and MTX was 31.36 µM and 44.99 µM, respectively. The V_max value of AcCPG2 for folate and MTX was 125.80 µmol/min/mg and 48.90  µmol/min/mg, respectively. Accordingly, thermostability and pH versatility makes AcCPG2 a potential biobetter variant for therapeutic applications.

Structural and thermodynamic analysis of factors governing the stability and thermal folding/unfolding of SazCA

Molecular basis of protein stability at different temperatures is a fundamental problem in protein science that is substantially far from being accurately and quantitatively solved as it requires an explicit knowledge of the temperature dependence of folding free energy of amino acid residues. In the present study, we attempted to gain insights into the thermodynamic stability of SazCA and its implications on protein folding/unfolding. We report molecular dynamics simulations of water solvated SazCA in a temperature range of 293-393 K to study the relationship between the thermostability and flexibility. Our structural analysis shows that the protein maintains the highest structural stability at 353 K and the protein conformations are highly flexible at temperatures above 353 K. Larger exposure of hydrophobic surface residues to the solvent medium for conformations beyond 353 K were identified from H-bond analysis. Higher number of secondary structure contents exhibited by SazCA at 353 K corroborated the conformations at 353 K to exhibit the highest thermal stability. The analysis of thermodynamics of protein stability revealed that the conformations that denature at higher melting temperatures tend to have greater maximum thermal stability. Our analysis shows that 353 K conformations have the highest melting temperature, which was found to be close to the experimental optimum temperature. The enhanced protein stability at 353 K due the least value of heat capacity at unfolding suggested an increase in folding. Comparative Gibbs free energy analysis and funnel shaped energy landscape confirmed a transition in folding/unfolding pathway of SazCA at 353 K.

Molecular modeling, docking and simulation dynamics of β-glucosidase reveals high-efficiency, thermo-stable, glucose tolerant enzyme in Paenibacillus lautus BHU3 strain

The enzyme β-glucosidase mediates the rate limiting step of conversion of cellobiose to glucose and thus plays a vital role in the process of cellulose degradation. The present study deals with analysis of the effective novel strain of Paenibacillus lautus BHU3 for identifying high-efficiency thermostable, glucose tolerant β-glucosidases. Seven counterparts with elevated T_m values ranging from 64.6 to 75.8 °C with high thermo-stability, were revealed through this analysis. The blind molecular docking of the model enzymes structures with cellobiose and pNPG gave high negative interaction energies ranging from -11.33 to -13.29 and -6.43 to -9.054 (kcal mol^-1), respectively. The enzyme WP_096774744.1 effectively formed 5 hydrogen bonds with the highest interaction energy (-13.29 kcal mol^-1) with cellobiose at its catalytic site. Molecular dynamics simulation analysis performed for the WP_096774744.1-pNPG complex predicted Glu5, Arg7, Lue68, Gly69 and Phe325 as the major contributing residues for accomplishing hydrolysis of β-1-4-linkage. Further, the molecular docking of WP_096774744.1 enzyme with glucose revealed a distinct glucose-binding site distant from the substrate-binding site, thus confirming the deficient competitive inhibition by glucose. Hence, WP_096774744.1 β-glucosidase appears to be an efficient enzyme with enhanced activity to biodegrade the cellulosic materials and highly relevant for waste management and various industrial applications.

A sequence embedding method for enzyme optimal condition analysis

Background

An enzyme activity is influenced by the external environment. It is important to have an enzyme remain high activity in a specific condition. A usual way is to first determine the optimal condition of an enzyme by either the gradient test or by tertiary structure, and then to use protein engineering to mutate a wild type enzyme for a higher activity in an expected condition.

Results

In this paper, we investigate the optimal condition of an enzyme by directly analyzing the sequence. We propose an embedding method to represent the amino acids and the structural information as vectors in the latent space. These vectors contain information about the correlations between amino acids and sites in the aligned amino acid sequences, as well as the correlation with the optimal condition. We crawled and processed the amino acid sequences in the glycoside hydrolase GH11 family, and got 125 amino acid sequences with optimal pH condition. We used probabilistic approximation method to implement the embedding learning method on these samples. Based on these embedding vectors, we design a computational score to determine which one has a better optimal condition for two given amino acid sequences and achieves the accuracy 80% on the test proteins in the same family. We also give the mutation suggestion such that it has a higher activity in an expected environment, which is consistent with the previously professional wet experiments and analysis.

Conclusion

A new computational method is proposed for the sequence based on the enzyme optimal condition analysis. Compared with the traditional process that involves a lot of wet experiments and requires multiple mutations, this method can give recommendations on the direction and location of amino acid substitution with reference significance for an expected condition in an efficient and effective way.

Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine

Protein stability predictions are becoming essential in medicine to develop novel immunotherapeutic agents and for drug discovery. Despite the large number of computational approaches for predicting the protein stability upon mutation, there are still critical unsolved problems: 1) the limited number of thermodynamic measurements for proteins provided by current databases; 2) the large intrinsic variability of ΔΔG values due to different experimental conditions; 3) biases in the development of predictive methods caused by ignoring the anti-symmetry of ΔΔG values between mutant and native protein forms; 4) over-optimistic prediction performance, due to sequence similarity between proteins used in training and test datasets. Here, we review these issues, highlighting new challenges required to improve current tools and to achieve more reliable predictions. In addition, we provide a perspective of how these methods will be beneficial for designing novel precision medicine approaches for several genetic disorders caused by mutations, such as cancer and neurodegenerative diseases.

Considerations on the Rational Design of Covalently Conjugated Cell-Penetrating Peptides (CPPs) for Intracellular Delivery of Proteins: A Guide to CPP Selection Using Glucarpidase as the Model Cargo Molecule

Access of proteins to their intracellular targets is limited by a hydrophobic barrier called the cellular membrane. Conjugation with cell-penetrating peptides (CPPs) has been shown to improve protein transduction into the cells. This conjugation can be either covalent or non-covalent, each with its unique pros and cons. The CPP-protein covalent conjugation may result in undesirable structural and functional alterations in the target protein. Therefore, we propose a systematic approach to evaluate different CPPs for covalent conjugations. This guide is presented using the carboxypeptidase G2 (CPG2) enzyme as the target protein. Seventy CPPs -out of 1155- with the highest probability of uptake efficiency were selected. These peptides were then conjugated to the N- or C-terminus of CPG2. Translational efficacy of the conjugates, robustness and thermodynamic properties of the chimera, aggregation possibility, folding rate, backbone flexibility, and aspects of in vivo administration such as protease susceptibility were predicted. The effect of the position of conjugation was evaluated using unpaired t-test (p < 0.05). It was concluded that N-terminal conjugation resulted in higher quality constructs. Seventeen CPP-CPG2/CPG2-CPP constructs were identified as the most promising. Based on this study, the bioinformatics workflow that is presented may be universally applied to any CPP-protein conjugate design.

ProTstab - predictor for cellular protein stability

BACKGROUND

Stability is one of the most fundamental intrinsic characteristics of proteins and can be determined with various methods. Characterization of protein properties does not keep pace with increase in new sequence data and therefore even basic properties are not known for far majority of identified proteins. There have been some attempts to develop predictors for protein stabilities; however, they have suffered from small numbers of known examples.

RESULTS

We took benefit of results from a recently developed cellular stability method, which is based on limited proteolysis and mass spectrometry, and developed a machine learning method using gradient boosting of regression trees. ProTstab method has high performance and is well suited for large scale prediction of protein stabilities.

CONCLUSIONS

The Pearson's correlation coefficient was 0.793 in 10-fold cross validation and 0.763 in independent blind test. The corresponding values for mean absolute error are 0.024 and 0.036, respectively. Comparison with a previously published method indicated ProTstab to have superior performance. We used the method to predict stabilities of all the remaining proteins in the entire human proteome and then correlated the predicted stabilities to protein chain lengths of isoforms and to localizations of proteins.

Proteome-wide Analysis of Protein Thermal Stability in the Model Higher Plant Arabidopsis thaliana

Modern tandem MS-based sequencing technologies allow for the parallel measurement of concentration and covalent modifications for proteins within a complex sample. Recently, this capability has been extended to probe a proteome's three-dimensional structure and conformational state by determining the thermal denaturation profile of thousands of proteins simultaneously. Although many animals and their resident microbes exist under a relatively narrow, regulated physiological temperature range, plants take on the often widely ranging temperature of their surroundings, possibly influencing the evolution of protein thermal stability. In this report we present the first in-depth look at the thermal proteome of a plant species, the model organism Arabidopsis thaliana By profiling the melting curves of over 1700 Arabidopsis proteins using six biological replicates, we have observed significant correlation between protein thermostability and several known protein characteristics, including molecular weight and the composition ratio of charged to polar amino acids. We also report on a divergence of the thermostability of the core and regulatory domains of the plant 26S proteasome that may reflect a unique property of the way protein turnover is regulated during temperature stress. Lastly, the highly replicated database of Arabidopsis melting temperatures reported herein provides baseline data on the variability of protein behavior in the assay. Unfolding behavior and experiment-to-experiment variability were observed to be protein-specific traits, and thus this data can serve to inform the design and interpretation of future targeted assays to probe the conformational status of proteins from plants exposed to different chemical, environmental and genetic challenges.

Computational analysis of the amino acid interactions that promote or decrease protein solubility

The solubility of globular proteins is a basic biophysical property that is usually a prerequisite for their functioning. In this study, we probed the solubility of globular proteins with the help of the statistical potential formalism, in view of objectifying the connection of solubility with structural and energetic properties and of the solubility-dependence of specific amino acid interactions. We started by setting up two independent datasets containing either soluble or aggregation-prone proteins with known structures. From these two datasets, we computed solubility-dependent distance potentials that are by construction biased towards the solubility of the proteins from which they are derived. Their analysis showed the clear preference of amino acid interactions such as Lys-containing salt bridges and aliphatic interactions to promote protein solubility, whereas others such as aromatic, His-π, cation-π, amino-π and anion-π interactions rather tend to reduce it. These results indicate that interactions involving delocalized π-electrons favor aggregation, unlike those involving no (or few) dispersion forces. Furthermore, using our potentials derived from either highly or weakly soluble proteins to compute protein folding free energies, we found that the difference between these two energies correlates better with solubility than other properties analyzed before such as protein length, isoelectric point and aliphatic index. This is, to the best of our knowledge, the first comprehensive in silico study of the impact of residue-residue interactions on protein solubility properties.The results of this analysis provide new insights that will facilitate future rational protein design applications aimed at modulating the solubility of targeted proteins.

Basic units of protein structure, folding, and function

Study of the hierarchy of domain structure with alternative sets of domains and analysis of discontinuous domains, consisting of remote segments of the polypeptide chain, raised a question about the minimal structural unit of the protein domain. The hypothesis on the decisive role of the polypeptide backbone in determining the elementary units of globular proteins have led to the discovery of closed loops. It is reviewed here how closed loops form the loop-n-lock structure of proteins, providing the foundation for stability and designability of protein folds/domain and underlying their co-translational folding. Simplified protein sequences are considered here with the aim to explore the basic principles that presumably dominated the folding and stability of proteins in the early stages of structural evolution. Elementary functional loops (EFLs), closed loops with one or few catalytic residues, are, in turn, units of the protein function. They are apparent descendants of the prebiotic ring-like peptides, which gave rise to the first functional folds/domains being fused in the beginning of the evolution of protein structure. It is also shown how evolutionary relations between protein functional superfamilies and folds delineated with the help of EFLs can contribute to establishing the rules for design of desired enzymatic functions. Generalized descriptors of the elementary functions are proposed to be used as basic units in the future computational design.

Predicting protein thermal stability changes upon point mutations using statistical potentials: Introducing HoTMuSiC

The accurate prediction of the impact of an amino acid substitution on the thermal stability of a protein is a central issue in protein science, and is of key relevance for the rational optimization of various bioprocesses that use enzymes in unusual conditions. Here we present one of the first computational tools to predict the change in melting temperature ΔT_m upon point mutations, given the protein structure and, when available, the melting temperature T_m of the wild-type protein. The key ingredients of our model structure are standard and temperature-dependent statistical potentials, which are combined with the help of an artificial neural network. The model structure was chosen on the basis of a detailed thermodynamic analysis of the system. The parameters of the model were identified on a set of more than 1,600 mutations with experimentally measured ΔT_m. The performance of our method was tested using a strict 5-fold cross-validation procedure, and was found to be significantly superior to that of competing methods. We obtained a root mean square deviation between predicted and experimental ΔT_m values of 4.2 °C that reduces to 2.9 °C when ten percent outliers are removed. A webserver-based tool is freely available for non-commercial use at soft.dezyme.com.

A quantitative model for oxygen uptake and release in a family of hemeproteins

MOTIVATION

Hemeproteins have many diverse functions that largely depend on the rate at which they uptake or release small ligands, like oxygen. These proteins have been extensively studied using either simulations or experiments, albeit only qualitatively and one or two proteins at a time.

RESULTS

We present a physical-chemical model, which uses data obtained exclusively from computer simulations, to describe the uptake and release of oxygen in a family of hemeproteins, called truncated hemoglobins (trHbs). Through a rigorous statistical analysis we demonstrate that our model successfully recaptures all the reported experimental oxygen association and dissociation kinetic rate constants, thus allowing us to establish the key factors that determine the rates at which these hemeproteins uptake and release oxygen. We found that internal tunnels as well as the distal site water molecules control ligand uptake, whereas oxygen stabilization by distal site residues controls ligand release. Because these rates largely determine the functions of these hemeproteins, these approaches will also be important tools in characterizing the trHbs members with unknown functions.

CONTACT

lboechi@ic.fcen.uba.ar

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Towards an accurate prediction of the thermal stability of homologous proteins

Protein thermostability has been the focus of growing research interests in the last decades since its understanding and control play important roles in the optimization of a wide series of bioprocesses of academic and industrial importance. The complexity of this issue is rooted in the fact that the mechanisms ensuring thermal resistance are not unique and specific, but rather family- or even protein-dependent. Therefore, and despite the amount of research already accomplished, obtaining fast and precise thermal stability predictions is still a challenge, especially on a large scale. This article deepens the study of protein thermal stability and is focused on the prediction of its best descriptor, the melting temperature Tm. The relations between Tm and a series of factors that are expected to influence the protein stability are analyzed and discussed. Different Tm-prediction methods that utilize these factors, sometimes with additional information about homologous proteins, are introduced, and their individual performances are evaluated. The best methods are based on temperature-dependent statistical potentials, on the environmental temperature of the host organism, on the fraction of charged residues, and on the number of residues. They are combined to build an improved prediction method with significantly increased score. The root mean square deviation between the computed and experimental Tm-values for 45 proteins of known structure from 11 families is about 7°C in cross-validation and decreases to 5°C when 10% outliers are removed. The associated linear correlation coefficients are equal to .91 and .95, respectively.

The hydrophobic temperature dependence of amino acids directly calculated from protein structures

The hydrophobic effect is the main driving force in protein folding. One can estimate the relative strength of this hydrophobic effect for each amino acid by mining a large set of experimentally determined protein structures. However, the hydrophobic force is known to be strongly temperature dependent. This temperature dependence is thought to explain the denaturation of proteins at low temperatures. Here we investigate if it is possible to extract this temperature dependence directly from a large set of protein structures determined at different temperatures. Using NMR structures filtered for sequence identity, we were able to extract hydrophobicity propensities for all amino acids at five different temperature ranges (spanning 265-340 K). These propensities show that the hydrophobicity becomes weaker at lower temperatures, in line with current theory. Alternatively, one can conclude that the temperature dependence of the hydrophobic effect has a measurable influence on protein structures. Moreover, this work provides a method for probing the individual temperature dependence of the different amino acid types, which is difficult to obtain by direct experiment.

In general, proteins become functional once they fold into a specific globular structure. On folding, hydrophobic amino acids get buried inside the protein such that they are shielded from the water; this hydrophobic effect makes a protein fold stable. However, the strength of the hydrophobicity is known to be strongly temperature dependent, leading for example to lower stability at lower temperatures (cold denaturation). Nevertheless, it is difficult to quantify the temperature dependence for hydrophobic amino acids. Here we are able to estimate the strength of the hydrophobic effect, by analysing the positions of a large number of amino acids from protein structures experimentally determined at different temperatures. For each amino acid type, we use the ratio between the number of residues at the inside and at the surface of the folded structures as a measure for its hydrophobicity. This approach shows that the hydrophobic effect becomes weaker at lower temperatures, as expected from theoretical predictions. Understanding the temperature dependence for amino acids, can help to make proteins (or enzymes) stable at a specific temperature range. For example, the design of enzymes that are stable and functional at low temperatures may benefit from this work.