Paulo Sérgio Lopes de Oliveira


Phone: +55 19 3512.1267




During last years, I have worked in the implantation and management of bioinformatics groups in Brazil. Nowadays, my group develops researches in genomic and transcriptome annotation and mainly in structural bioinformatics, with development of algorithms for analyses of protein-protein interactions, and protein-DNA interactions.


Algorithms for identification of cavities in proteins

The identification and characterization of geometrical and physical-chemical properties in protein vacant spaces aggregates important information for steering rational drug design and functional characterization of binding and catalytic sites. Therefore, several softwares have been developed during the past two decades in order to perform such characterization. Nevertheless, the existing tools still present a series of limitations such as lack of precision, lack of integrability in large scale protocols, lack of customization capacity and the lack of a proper electrostatic depiction. In order to complement and extend the functionality of existing softwares, providing a systematic and more descriptive portrayal of protein vacant spaces, we are developing new algorithms to approach the problem. By employing a user-driven matrix modeling, our tool identifies and characterizes empty spaces in all sorts of protein topologies. The software quantifies the volume, the area and the shape of the surface, the residues that interact with the vacant spaces and a partial charge map of the computed surface. Our routine was integrated with a graphical molecular modeling software, providing the user with a simple and easy-to-use interface. It has been validated with a distinct set of proteins and binding sites. Compared with existing software, our algoritm presents greater precision, greater accessibility and ease of integration in large scale protocols and visualization softwares. Also, the software possesses unique and innovative features such as the ability to segment and subsegment the empty spaces, a electrostatic depiction and a ligand interaction highlight feature.

Collaborators: Saulo Pires Oliveira, Dr. Tiago Sobreira (LNBio), , Dr. José Xavier-Neto and Rodrigo Honorato

Different kinds of cavities detected by our cavity identification algorithm

MAD (Multidomain Assemble Design and protocol)

During molecular evolution protein domains have acted as building blocks, being combined in different ways, for instance, modulating protein function. These protein structures recurring in proteomes are known as conserved domains. A good understanding of how multi domain proteins were constructed is essential for understanding their context and their evolutionary relationships with other domains. Many of the protein-protein interactions (PPI) or intra protein interactions are mediated by interactions between domains and specific patterns of sequence. In multi domain protein domains are usually connected by flexible connectors. This conformational flexibility induces great difficulty in obtaining experimental structure, often impairing the use of experimental techniques. Aiming to offer reliable models for these proteins, we present the MAD (Multidomain Assemble Design and protocol).  After identification and modeling, it is possible to identify the intra-domain connectors, and applied an ab initio modeling through pyRosetta. The next step consists of a rigid body docking procedure, seeking the most favorable interaction interfaces and subsequent clustering of conformations by spatial similarity. Here we consider that each representative of a group is a possible arrangement of multi domain protein, being more or less favorable. We evaluated: homology modeling quality, energetic and conformational distribution of connectors, PPI profile between domains, similarity to crystal structure and final model after energy minimization. The ab initio modeling provided us with a series of directions towards possible conformational states of the multi domain protein. We generated one or more three-dimensional models for each protein of the validation set of validation.  The total amount of final models varies given the number of restrictions gathered from the connector modeling and after clustering possible dock conformations.

Collaborators: Dr. Tiago Sobreira (LNBio), and Rodrigo Honorato

Distribuition of domain interaction energy in a multidomain proteins.

The understanding of substrate binding sites in protein kinases by binding energy profiles

Protein kinases have an important role in cell signaling by coordinating various cellular processes, including metabolism, growth, division, differentiation, mobility, transport through membranes, contraction, and apoptosis being phosphorylation the most abundant and important type of cellular regulation. Around 30% of all cellular proteins are supposed to be phosphorylated on at least one residue. Consequently, changes in protein kinases lead to a large number of pathologies such as inflammation, autoimmune diseases, cancers and heart disease. On this hand, protein kinases are attractive targets for drug development, including an estimative that about 25% of the pharmaceutical industry’s efforts are focused on developing inhibitors for kinases. However, most inhibitors, acting in the ATP binding site; hence, because of high active-site similarities of closely related kinases, specificity issues are critical considerations for current inhibitor development to avoid unwanted side-effects caused by inhibition of anti-targets. Under these circumstances, the use of peptides, which copy ‘natural’ motifs that specifically influence kinase activity and/or its intracellular interactions with cognate partners, may be a promising approach for selective inhibition of protein kinases. In the present work, we describe a new  computational method for characterization of substrate binding sites in kinases using peptide binding energy profiles. Using information from literature and known crystallographic structures of protein kinases were assembled protein-peptide complexes through molecular modeling. These complexes were submitted to methods of molecular mechanics and calculations of binding energy using the method of Poisson-Boltzmann. Multivariate regression models correlating energetic parameters and experimental kinetic measures as Km or inhibition constants (Ki) unveiled that is possible to use theoretical measures to estimate affinity constant of substrate peptides and kinases.

Collaborators: Dr. Tiago Sobreira (LNBio), Dr. Deborah Schechtman (IQ-USP) and Dr. Artur Cordeiro (LNBio)




1998 – 2000



Ludwig institute for Cancer Research- São Paulo’s branch – Brazil

1994 – 1998

Ph.D in Chemistry (sub-area physical chemistry)

University of São Paulo, USP, Brasil

1991 – 1994

M.Sc in Chemistry (sub-area analytical chemistry)

University of São Paulo, USP, Brasil

1987 – 1991

Bachelor in Biological Sciences

University of State of São Paulo, UNESP, Brazil



Sobreira TJ, Marlétaz F, Simões-Costa M, Schechtman D, Pereira AC, Brunet F,Sweeney S, Pani A, Aronowicz J, Lowe CJ, Davidson B, Laudet V, Bronner M, de  Oliveira PS, Schubert M, Xavier-Neto J. Structural shifts of aldehyde  dehydrogenase enzymes were instrumental for the early evolution of  retinoid-dependent axial patterning in metazoans. Proc Natl Acad Sci U S A. 2011

Almeida MA, Oliveira PS, Pereira TV, Krieger JE, Pereira AC. An empirical  evaluation of  imputation accuracy for association statistics reveals increased  type-I error rates in genome-wide associations. BMC Genet. Jan 20;12:10, 2011

Castillo HA, Cravo RM,  Azambuja AP,  Simões-Costa MS, Sura-Trueba S, Gonzalez J, Slonimsky E, Almeida K, Abreu JG,  de Almeida MAA, Sobreira TP, de Oliveira SHP,  de Oliveira PS,  Signore IA, Colombo A, Concha ML, Spengler TS, Bronner-Fraser M, Nobrega M, Rosenthal N , Xavier-Neto J. Insights into the organization of dorsal spinal cord pathways from an evolutionarily conserved raldh2 intronic enhancer .Development 137, . 2010

Ojopi E , de Oliveira PS, Nunes DN,  Paquola ACM, Demarco R, Gregorio SP, Aires KA, Menck CFM, Leite LCC. ; VERJOVSKI-ALMEIDA S, Dias Neto E. A quantitative view of the transcriptome of Schistosoma mansoni adult-worms using SAGE. BMC Genomics, v. 21, p. 186-197, 2007.

Pereira TV ; Rudnicki M, Cheung BMY,  Baum L, Yamada Y, de Oliveira PS , Pereira AC, Krieger JE. Three endothelial nitric oxide (NOS3) gene polymorphisms in hypertensive and normotensive individuals: meta-analysis of 53 studies reveals evidence of publication bias. Journal of Hypertension, v. 25, p. 1763-1774, 2007.

Bertola DR, Pereira AC, de Oliveira PS, Kim CA, Krieger JE.  Clinical variability in a Noonan syndrome family with a new PTPN11 gene mutation. Am J Med Genet A. 2004 Nov 1;130(4):378-83.

Transcript Finishing Iniative Consortium,  Sogayar MC, Camargo AA.  A Transcript Finishing Initiative for Closing Gaps in the Human Transcriptome. Genome Res. 2004 Jun 14.

Sakabe NJ, de Souza JE, Galante PA, de Oliveira PS, Passetti F, Brentani H, Osorio EC, Zaiats AC, Leerkes MR, Kitajima JP, Brentani RR, Strausberg RL, Simpson AJ, de Souza SJ.  ORESTES are enriched in rare exon usage variants affecting the encoded proteins. C R Biol. 2003 Oct-Nov;326(10-11):979-85.

De Souza GA, Oliveira PS, Trapani S, Santos AC, Rosa JC, Laure HJ, Faca VM, Correia MT, Tavares GA, Oliva G, Coelho LC, Greene LJ.   Amino acid sequence and tertiary structure of Cratylia mollis seed lectin. Glycobiology. 2003 Dec;13(12):961-72.

Dias Neto E, Correa RG, Verjovski-Almeida S, Briones M, Nagai MA, da Silva Junior W, Zago MA, Bordin S, Costa FF, Goldman GH, Carvalho AF,  Matsukuma A, Baia G, Simpson D, Brunstein A, de Oliveira PSL,  Jongeneel CV, O’Hare MJ, Soares F,  Brentani RR, Reis LFL, de Souza SJ, Simpson AJG. Shotgun Sequencing of the Human Transcriptome with Open Reading Frame Expressed Sequence Tags. Proc. Natl. Acad. Sci. U.S.A. Mar 28;97(7):3491-6.,2000.

Rosa JC, de Oliveira PSL, Garratt R, Beltramini LM, Resing K , Roque-Barreira MC, Greene LJ. KM+, a mannose-binding lectin from Artocarpus integrifolia: Amino acid sequence, predicted tertiary structure, carbohydrate recognition and analysis of the beta-prism fold. Protein Sciences,(8)1:13-24, 1999.