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Deep Analysis of Residue Constraints (DARC): identifying determinants of protein functional specificity. Sci Rep 2020; 10:1691. [PMID: 32015389 PMCID: PMC6997377 DOI: 10.1038/s41598-019-55118-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 11/23/2019] [Indexed: 01/03/2023] Open
Abstract
Protein functional constraints are manifest as superfamily and functional-subgroup conserved residues, and as pairwise correlations. Deep Analysis of Residue Constraints (DARC) aids the visualization of these constraints, characterizes how they correlate with each other and with structure, and estimates statistical significance. This can identify determinants of protein functional specificity, as we illustrate for bacterial DNA clamp loader ATPases. These load ring-shaped sliding clamps onto DNA to keep polymerase attached during replication and contain one δ, three γ, and one δ’ AAA+ subunits semi-circularly arranged in the order δ-γ1-γ2-γ3-δ’. Only γ is active, though both γ and δ’ functionally influence an adjacent γ subunit. DARC identifies, as functionally-congruent features linking allosterically the ATP, DNA, and clamp binding sites: residues distinctive of γ and of γ/δ’ that mutually interact in trans, centered on the catalytic base; several γ/δ’-residues and six γ/δ’-covariant residue pairs within the DNA binding N-termini of helices α2 and α3; and γ/δ’-residues associated with the α2 C-terminus and the clamp-binding loop. Most notable is a trans-acting γ/δ’ hydroxyl group that 99% of other AAA+ proteins lack. Mutation of this hydroxyl to a methyl group impedes clamp binding and opening, DNA binding, and ATP hydrolysis—implying a remarkably clamp-loader-specific function.
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A Single Mutation Increases the Thermostability and Activity of Aspergillus terreus Amine Transaminase. Molecules 2019; 24:molecules24071194. [PMID: 30934681 PMCID: PMC6479498 DOI: 10.3390/molecules24071194] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 03/21/2019] [Accepted: 03/23/2019] [Indexed: 11/17/2022] Open
Abstract
Enhancing the thermostability of (R)-selective amine transaminases (AT-ATA) will expand its application in the asymmetric synthesis of chiral amines. In this study, mutual information and coevolution networks of ATAs were analyzed by the Mutual Information Server to Infer Coevolution (MISTIC). Subsequently, the amino acids most likely to influence the stability and function of the protein were investigated by alanine scanning and saturation mutagenesis. Four stabilized mutants (L118T, L118A, L118I, and L118V) were successfully obtained. The best mutant, L118T, exhibited an improved thermal stability with a 3.7-fold enhancement in its half-life (t1/2) at 40 °C and a 5.3 °C increase in T5010 compared to the values for the wild-type protein. By the differential scanning fluorimetry (DSF) analysis, the best mutant, L118T, showed a melting temperature (Tm) of 46.4 °C, which corresponded to a 5.0 °C increase relative to the wild-type AT-ATA (41.4 °C). Furthermore, the most stable mutant L118T displayed the highest catalytic efficiency among the four stabilized mutants.
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Stocchi N, Revuelta MV, Castronuovo PAL, Vera DMA, Ten Have A. Molecular dynamics and structure function analysis show that substrate binding and specificity are major forces in the functional diversification of Eqolisins. BMC Bioinformatics 2018; 19:338. [PMID: 30249179 PMCID: PMC6154417 DOI: 10.1186/s12859-018-2348-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 08/29/2018] [Indexed: 11/30/2022] Open
Abstract
Background Eqolisins are rare acid proteases found in archaea, bacteria and fungi. Certain fungi secrete acids as part of their lifestyle and interestingly these also have many eqolisin paralogs, up to nine paralogs have been recorded. This suggests a process of functional redundancy and diversification has occurred, which was the subject of the research we performed and describe here. Results We identified eqolisin homologs by means of iterative HMMER analysis of the NR database. The identified sequences were scrutinized for which new hallmarks were identified by molecular dynamics simulations of mutants in highly conserved positions, using the structure of an eqolisin that was crystallized in the presence of a transition state inhibitor. Four conserved glycines were shown to be important for functionality. A substitution of W67F is shown to be accompanied by the L105W substitution. Molecular dynamics shows that the W67 binds to the substrate via a π-π stacking and a salt bridge, the latter being stronger in a virtual W67F/L105W double mutant of the resolved structure of Scytalido-carboxyl peptidase-B (PDB ID: 2IFW). Additional problematic mutations are discussed. Upon sequence scrutiny we obtained a set of 233 sequences that was used to reconstruct a Bayesian phylogenetic tree. We identified 14 putative specificity determining positions (SDPs) of which four are explained by mere structural explanations and nine seem to correspond to functional diversification related with substrate binding and specificity. A first sub-network of SDPs is related to substrate specificity whereas the second sub-network seems to affect the dynamics of three loops that are involved in substrate binding. Conclusion The eqolisins form a small superfamily of acid proteases with nevertheless many paralogs in acidic fungi. Functional redundancy has resulted in diversification related to substrate specificity and substrate binding. Electronic supplementary material The online version of this article (10.1186/s12859-018-2348-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Nicolás Stocchi
- Instituto de Investigaciones Biológicas (IIB-CONICET-UNMdP), Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, CC 1245, 7600, Mar del Plata, Argentina
| | - María Victoria Revuelta
- Instituto de Investigaciones Biológicas (IIB-CONICET-UNMdP), Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, CC 1245, 7600, Mar del Plata, Argentina.,Pressent address: Department of Medicine, Hematology and Oncology Division, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Priscila Ailín Lanza Castronuovo
- QUIAMM-INBIOTEC-CONICET, Department of Chemistry - Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, Funes 3350, 7600, Mar del Plata, Argentina
| | - D Mariano A Vera
- QUIAMM-INBIOTEC-CONICET, Department of Chemistry - Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, Funes 3350, 7600, Mar del Plata, Argentina
| | - Arjen Ten Have
- Instituto de Investigaciones Biológicas (IIB-CONICET-UNMdP), Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata, CC 1245, 7600, Mar del Plata, Argentina.
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Neuwald AF, Aravind L, Altschul SF. Inferring joint sequence-structural determinants of protein functional specificity. eLife 2018; 7. [PMID: 29336305 PMCID: PMC5770160 DOI: 10.7554/elife.29880] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 12/22/2017] [Indexed: 01/05/2023] Open
Abstract
Residues responsible for allostery, cooperativity, and other subtle but functionally important interactions remain difficult to detect. To aid such detection, we employ statistical inference based on the assumption that residues distinguishing a protein subgroup from evolutionarily divergent subgroups often constitute an interacting functional network. We identify such networks with the aid of two measures of statistical significance. One measure aids identification of divergent subgroups based on distinguishing residue patterns. For each subgroup, a second measure identifies structural interactions involving pattern residues. Such interactions are derived either from atomic coordinates or from Direct Coupling Analysis scores, used as surrogates for structural distances. Applying this approach to N-acetyltransferases, P-loop GTPases, RNA helicases, synaptojanin-superfamily phosphatases and nucleases, and thymine/uracil DNA glycosylases yielded results congruent with biochemical understanding of these proteins, and also revealed striking sequence-structural features overlooked by other methods. These and similar analyses can aid the design of drugs targeting allosteric sites.
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Affiliation(s)
- Andrew F Neuwald
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, United States.,Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, United States
| | - L Aravind
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, United States
| | - Stephen F Altschul
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, United States
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Neuwald AF, Altschul SF. Inference of Functionally-Relevant N-acetyltransferase Residues Based on Statistical Correlations. PLoS Comput Biol 2016; 12:e1005294. [PMID: 28002465 PMCID: PMC5225019 DOI: 10.1371/journal.pcbi.1005294] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Revised: 01/10/2017] [Accepted: 12/08/2016] [Indexed: 11/25/2022] Open
Abstract
Over evolutionary time, members of a superfamily of homologous proteins sharing a common structural core diverge into subgroups filling various functional niches. At the sequence level, such divergence appears as correlations that arise from residue patterns distinct to each subgroup. Such a superfamily may be viewed as a population of sequences corresponding to a complex, high-dimensional probability distribution. Here we model this distribution as hierarchical interrelated hidden Markov models (hiHMMs), which describe these sequence correlations implicitly. By characterizing such correlations one may hope to obtain information regarding functionally-relevant properties that have thus far evaded detection. To do so, we infer a hiHMM distribution from sequence data using Bayes’ theorem and Markov chain Monte Carlo (MCMC) sampling, which is widely recognized as the most effective approach for characterizing a complex, high dimensional distribution. Other routines then map correlated residue patterns to available structures with a view to hypothesis generation. When applied to N-acetyltransferases, this reveals sequence and structural features indicative of functionally important, yet generally unknown biochemical properties. Even for sets of proteins for which nothing is known beyond unannotated sequences and structures, this can lead to helpful insights. We describe, for example, a putative coenzyme-A-induced-fit substrate binding mechanism mediated by arginine residue switching between salt bridge and π-π stacking interactions. A suite of programs implementing this approach is available (psed.igs.umaryland.edu). Protein sequence data, when gathered in great quantity, contain important but implicit biological information manifest as statistical correlations. Here we describe an approach to access this information by comprehensively modeling and characterizing the distribution of sequences belonging to a major protein superfamily. This approach takes as input a large set of unaligned sequences belonging to the superfamily. By applying the minimum description length principle, it seeks the statistical model that best explains the sequences while avoiding over-fitting the data. It concurrently aligns the sequences and, to model evolutionary divergence, partitions them into subgroups that are hierarchically-arranged based upon correlated residue patterns. Auxiliary routines create PyMOL scripts to visualize the locations of correlated residues within available structures. Because these correlations likely arise from structural and biochemical constraints, they can help elucidate protein properties important for functional specificity. Comparing and contrasting sequence and structural features in this way may therefore suggest, in the light of published studies, plausible biological hypotheses for experimental investigation. We illustrate this approach with N-acetyltransferases.
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Affiliation(s)
- Andrew F. Neuwald
- Institute for Genome Sciences and Department of Biochemistry & Molecular Biology, University of Maryland School of Medicine, BioPark II, Room 617, Baltimore, MD, United States of America
- * E-mail:
| | - Stephen F. Altschul
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States of America
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Wagner JR, Lee CT, Durrant JD, Malmstrom RD, Feher VA, Amaro RE. Emerging Computational Methods for the Rational Discovery of Allosteric Drugs. Chem Rev 2016; 116:6370-90. [PMID: 27074285 PMCID: PMC4901368 DOI: 10.1021/acs.chemrev.5b00631] [Citation(s) in RCA: 148] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
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Allosteric drug development holds
promise for delivering medicines
that are more selective and less toxic than those that target orthosteric
sites. To date, the discovery of allosteric binding sites and lead
compounds has been mostly serendipitous, achieved through high-throughput
screening. Over the past decade, structural data has become more readily
available for larger protein systems and more membrane protein classes
(e.g., GPCRs and ion channels), which are common allosteric drug targets.
In parallel, improved simulation methods now provide better atomistic
understanding of the protein dynamics and cooperative motions that
are critical to allosteric mechanisms. As a result of these advances,
the field of predictive allosteric drug development is now on the
cusp of a new era of rational structure-based computational methods.
Here, we review algorithms that predict allosteric sites based on
sequence data and molecular dynamics simulations, describe tools that
assess the druggability of these pockets, and discuss how Markov state
models and topology analyses provide insight into the relationship
between protein dynamics and allosteric drug binding. In each section,
we first provide an overview of the various method classes before
describing relevant algorithms and software packages.
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Affiliation(s)
- Jeffrey R Wagner
- Department of Chemistry & Biochemistry and ‡National Biomedical Computation Resource, University of California, San Diego , La Jolla, California 92093, United States
| | - Christopher T Lee
- Department of Chemistry & Biochemistry and ‡National Biomedical Computation Resource, University of California, San Diego , La Jolla, California 92093, United States
| | - Jacob D Durrant
- Department of Chemistry & Biochemistry and ‡National Biomedical Computation Resource, University of California, San Diego , La Jolla, California 92093, United States
| | - Robert D Malmstrom
- Department of Chemistry & Biochemistry and ‡National Biomedical Computation Resource, University of California, San Diego , La Jolla, California 92093, United States
| | - Victoria A Feher
- Department of Chemistry & Biochemistry and ‡National Biomedical Computation Resource, University of California, San Diego , La Jolla, California 92093, United States
| | - Rommie E Amaro
- Department of Chemistry & Biochemistry and ‡National Biomedical Computation Resource, University of California, San Diego , La Jolla, California 92093, United States
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